{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variable Name</th>\n",
       "      <th>Description</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SeriousDlqin2yrs</td>\n",
       "      <td>Person experienced 90 days past due delinquenc...</td>\n",
       "      <td>Y/N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RevolvingUtilizationOfUnsecuredLines</td>\n",
       "      <td>Total balance on credit cards and personal lin...</td>\n",
       "      <td>percentage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>age</td>\n",
       "      <td>Age of borrower in years</td>\n",
       "      <td>integer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NumberOfTime30-59DaysPastDueNotWorse</td>\n",
       "      <td>Number of times borrower has been 30-59 days p...</td>\n",
       "      <td>integer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DebtRatio</td>\n",
       "      <td>Monthly debt payments, alimony,living costs di...</td>\n",
       "      <td>percentage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MonthlyIncome</td>\n",
       "      <td>Monthly income</td>\n",
       "      <td>real</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NumberOfOpenCreditLinesAndLoans</td>\n",
       "      <td>Number of Open loans (installment like car loa...</td>\n",
       "      <td>integer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NumberOfTimes90DaysLate</td>\n",
       "      <td>Number of times borrower has been 90 days or m...</td>\n",
       "      <td>integer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NumberRealEstateLoansOrLines</td>\n",
       "      <td>Number of mortgage and real estate loans inclu...</td>\n",
       "      <td>integer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NumberOfTime60-89DaysPastDueNotWorse</td>\n",
       "      <td>Number of times borrower has been 60-89 days p...</td>\n",
       "      <td>integer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NumberOfDependents</td>\n",
       "      <td>Number of dependents in family excluding thems...</td>\n",
       "      <td>integer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Variable Name  \\\n",
       "0                       SeriousDlqin2yrs   \n",
       "1   RevolvingUtilizationOfUnsecuredLines   \n",
       "2                                    age   \n",
       "3   NumberOfTime30-59DaysPastDueNotWorse   \n",
       "4                              DebtRatio   \n",
       "5                          MonthlyIncome   \n",
       "6        NumberOfOpenCreditLinesAndLoans   \n",
       "7                NumberOfTimes90DaysLate   \n",
       "8           NumberRealEstateLoansOrLines   \n",
       "9   NumberOfTime60-89DaysPastDueNotWorse   \n",
       "10                    NumberOfDependents   \n",
       "\n",
       "                                          Description        Type  \n",
       "0   Person experienced 90 days past due delinquenc...         Y/N  \n",
       "1   Total balance on credit cards and personal lin...  percentage  \n",
       "2                            Age of borrower in years     integer  \n",
       "3   Number of times borrower has been 30-59 days p...     integer  \n",
       "4   Monthly debt payments, alimony,living costs di...  percentage  \n",
       "5                                      Monthly income        real  \n",
       "6   Number of Open loans (installment like car loa...     integer  \n",
       "7   Number of times borrower has been 90 days or m...     integer  \n",
       "8   Number of mortgage and real estate loans inclu...     integer  \n",
       "9   Number of times borrower has been 60-89 days p...     integer  \n",
       "10  Number of dependents in family excluding thems...     integer  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 标量的基本情况，变量名称、描述和类型\n",
    "pd.read_excel(\"./GiveMeSomeCredit/Data Dictionary.xls\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>变量名字</th>\n",
       "      <th>描述</th>\n",
       "      <th>类型</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SeriousDlqin2yrs</td>\n",
       "      <td>好坏客户</td>\n",
       "      <td>Y/N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RevolvingUtilizationOfUnsecuredLines</td>\n",
       "      <td>信用卡和个人信用额度的总余额，除了房地产和没有分期付款债务，如汽车贷款除以信用额度</td>\n",
       "      <td>percentage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>age</td>\n",
       "      <td>出生年龄</td>\n",
       "      <td>integer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NumberOfTime30-59DaysPastDueNotWorse</td>\n",
       "      <td>过去两年借款35-59天逾期但是不糟糕的次数</td>\n",
       "      <td>integer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DebtRatio</td>\n",
       "      <td>每月债务支付、赡养费、生活费用除以毛利</td>\n",
       "      <td>percentage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MonthlyIncome</td>\n",
       "      <td>月收入</td>\n",
       "      <td>real</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NumberOfOpenCreditLinesAndLoans</td>\n",
       "      <td>开放式贷款（分期付款汽车贷款或抵押贷款）和信用额度（信用卡）的次数</td>\n",
       "      <td>integer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NumberOfTimes90DaysLate</td>\n",
       "      <td>&gt;= 90天逾期</td>\n",
       "      <td>integer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NumberRealEstateLoansOrLines</td>\n",
       "      <td>抵押贷款和房地产贷款包括房屋净值信贷额度</td>\n",
       "      <td>integer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NumberOfTime60-89DaysPastDueNotWorse</td>\n",
       "      <td>过去两年借款60-89天逾期但是不糟糕的次数</td>\n",
       "      <td>integer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NumberOfDependents</td>\n",
       "      <td>家属数量不包括自己</td>\n",
       "      <td>integer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    变量名字  \\\n",
       "0                       SeriousDlqin2yrs   \n",
       "1   RevolvingUtilizationOfUnsecuredLines   \n",
       "2                                    age   \n",
       "3   NumberOfTime30-59DaysPastDueNotWorse   \n",
       "4                              DebtRatio   \n",
       "5                          MonthlyIncome   \n",
       "6        NumberOfOpenCreditLinesAndLoans   \n",
       "7                NumberOfTimes90DaysLate   \n",
       "8           NumberRealEstateLoansOrLines   \n",
       "9   NumberOfTime60-89DaysPastDueNotWorse   \n",
       "10                    NumberOfDependents   \n",
       "\n",
       "                                           描述          类型  \n",
       "0                                        好坏客户         Y/N  \n",
       "1   信用卡和个人信用额度的总余额，除了房地产和没有分期付款债务，如汽车贷款除以信用额度  percentage  \n",
       "2                                        出生年龄     integer  \n",
       "3                      过去两年借款35-59天逾期但是不糟糕的次数     integer  \n",
       "4                         每月债务支付、赡养费、生活费用除以毛利  percentage  \n",
       "5                                         月收入        real  \n",
       "6           开放式贷款（分期付款汽车贷款或抵押贷款）和信用额度（信用卡）的次数     integer  \n",
       "7                                    >= 90天逾期     integer  \n",
       "8                        抵押贷款和房地产贷款包括房屋净值信贷额度     integer  \n",
       "9                      过去两年借款60-89天逾期但是不糟糕的次数     integer  \n",
       "10                                  家属数量不包括自己     integer  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel(\"./GiveMeSomeCredit/数据属性.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./GiveMeSomeCredit/cs-training.csv\").drop(\"Unnamed: 0\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.766127</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.957151</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.658180</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0.085113</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.233810</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036050</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.907239</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>63588.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "0                 1                              0.766127   45   \n",
       "1                 0                              0.957151   40   \n",
       "2                 0                              0.658180   38   \n",
       "3                 0                              0.233810   30   \n",
       "4                 0                              0.907239   49   \n",
       "\n",
       "   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "0                                     2   0.802982         9120.0   \n",
       "1                                     0   0.121876         2600.0   \n",
       "2                                     1   0.085113         3042.0   \n",
       "3                                     0   0.036050         3300.0   \n",
       "4                                     1   0.024926        63588.0   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                               13                        0   \n",
       "1                                4                        0   \n",
       "2                                2                        1   \n",
       "3                                5                        0   \n",
       "4                                7                        0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                             6                                     0   \n",
       "1                             0                                     0   \n",
       "2                             0                                     0   \n",
       "3                             0                                     0   \n",
       "4                             1                                     0   \n",
       "\n",
       "   NumberOfDependents  \n",
       "0                 2.0  \n",
       "1                 1.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 11 columns):\n",
      "SeriousDlqin2yrs                        150000 non-null int64\n",
      "RevolvingUtilizationOfUnsecuredLines    150000 non-null float64\n",
      "age                                     150000 non-null int64\n",
      "NumberOfTime30-59DaysPastDueNotWorse    150000 non-null int64\n",
      "DebtRatio                               150000 non-null float64\n",
      "MonthlyIncome                           120269 non-null float64\n",
      "NumberOfOpenCreditLinesAndLoans         150000 non-null int64\n",
      "NumberOfTimes90DaysLate                 150000 non-null int64\n",
      "NumberRealEstateLoansOrLines            150000 non-null int64\n",
      "NumberOfTime60-89DaysPastDueNotWorse    150000 non-null int64\n",
      "NumberOfDependents                      146076 non-null float64\n",
      "dtypes: float64(4), int64(7)\n",
      "memory usage: 12.6 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过 info() 可以看出一共有150000条信息，MonthyIncome 和 NumberOfDependents 存在缺失的情况，MonthyIncome 缺失29731个数据，NumberOfDependents 缺失3924个数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面使用 describe() 来看数据集中的计数、均值、最大最小值、标准差和第一、二、三个四分位值，同时增加了缺失率的计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>missing_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>0.066840</td>\n",
       "      <td>0.249746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>6.048438</td>\n",
       "      <td>249.755371</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029867</td>\n",
       "      <td>0.154181</td>\n",
       "      <td>0.559046</td>\n",
       "      <td>50708.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>52.295207</td>\n",
       "      <td>14.771866</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>0.421033</td>\n",
       "      <td>4.192781</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DebtRatio</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>353.005076</td>\n",
       "      <td>2037.818523</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.175074</td>\n",
       "      <td>0.366508</td>\n",
       "      <td>0.868254</td>\n",
       "      <td>329664.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <td>120269.0</td>\n",
       "      <td>6670.221237</td>\n",
       "      <td>14384.674215</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3400.000000</td>\n",
       "      <td>5400.000000</td>\n",
       "      <td>8249.000000</td>\n",
       "      <td>3008750.0</td>\n",
       "      <td>0.198207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>8.452760</td>\n",
       "      <td>5.145951</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>0.265973</td>\n",
       "      <td>4.169304</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>1.018240</td>\n",
       "      <td>1.129771</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>0.240387</td>\n",
       "      <td>4.155179</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <td>146076.0</td>\n",
       "      <td>0.757222</td>\n",
       "      <td>1.115086</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.026160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         count         mean           std  \\\n",
       "SeriousDlqin2yrs                      150000.0     0.066840      0.249746   \n",
       "RevolvingUtilizationOfUnsecuredLines  150000.0     6.048438    249.755371   \n",
       "age                                   150000.0    52.295207     14.771866   \n",
       "NumberOfTime30-59DaysPastDueNotWorse  150000.0     0.421033      4.192781   \n",
       "DebtRatio                             150000.0   353.005076   2037.818523   \n",
       "MonthlyIncome                         120269.0  6670.221237  14384.674215   \n",
       "NumberOfOpenCreditLinesAndLoans       150000.0     8.452760      5.145951   \n",
       "NumberOfTimes90DaysLate               150000.0     0.265973      4.169304   \n",
       "NumberRealEstateLoansOrLines          150000.0     1.018240      1.129771   \n",
       "NumberOfTime60-89DaysPastDueNotWorse  150000.0     0.240387      4.155179   \n",
       "NumberOfDependents                    146076.0     0.757222      1.115086   \n",
       "\n",
       "                                      min          25%          50%  \\\n",
       "SeriousDlqin2yrs                      0.0     0.000000     0.000000   \n",
       "RevolvingUtilizationOfUnsecuredLines  0.0     0.029867     0.154181   \n",
       "age                                   0.0    41.000000    52.000000   \n",
       "NumberOfTime30-59DaysPastDueNotWorse  0.0     0.000000     0.000000   \n",
       "DebtRatio                             0.0     0.175074     0.366508   \n",
       "MonthlyIncome                         0.0  3400.000000  5400.000000   \n",
       "NumberOfOpenCreditLinesAndLoans       0.0     5.000000     8.000000   \n",
       "NumberOfTimes90DaysLate               0.0     0.000000     0.000000   \n",
       "NumberRealEstateLoansOrLines          0.0     0.000000     1.000000   \n",
       "NumberOfTime60-89DaysPastDueNotWorse  0.0     0.000000     0.000000   \n",
       "NumberOfDependents                    0.0     0.000000     0.000000   \n",
       "\n",
       "                                              75%        max  missing_rate  \n",
       "SeriousDlqin2yrs                         0.000000        1.0      0.000000  \n",
       "RevolvingUtilizationOfUnsecuredLines     0.559046    50708.0      0.000000  \n",
       "age                                     63.000000      109.0      0.000000  \n",
       "NumberOfTime30-59DaysPastDueNotWorse     0.000000       98.0      0.000000  \n",
       "DebtRatio                                0.868254   329664.0      0.000000  \n",
       "MonthlyIncome                         8249.000000  3008750.0      0.198207  \n",
       "NumberOfOpenCreditLinesAndLoans         11.000000       58.0      0.000000  \n",
       "NumberOfTimes90DaysLate                  0.000000       98.0      0.000000  \n",
       "NumberRealEstateLoansOrLines             2.000000       54.0      0.000000  \n",
       "NumberOfTime60-89DaysPastDueNotWorse     0.000000       98.0      0.000000  \n",
       "NumberOfDependents                       1.000000       20.0      0.026160  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T.assign(missing_rate = df.apply(lambda x : (len(x)-x.count())/float(len(x))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 缺失值处理：\n",
    "    1、由于 MonthyIncome 缺失较多，因此不适合直接进行删除，这边根据变量之间存在的关系来填补缺失值，并采用随机森林的方法；\n",
    "    \n",
    "    2、NumberOfDependents 的缺失较少，对于整体的样本影响有限，因此这边直接进行删除操作，后续也可以进行一些其他的填补操作。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "# 用随机森林对缺失值预测填充函数\n",
    "def set_missing(df):\n",
    "    # 把已有的数值型特征取出来\n",
    "    process_df = df.iloc[:, [5,0,1,2,3,4,6,7,8,9]]\n",
    "    # 分成已知该特征和未知该特征两部分\n",
    "    known = process_df[process_df.MonthlyIncome.notnull()].as_matrix()\n",
    "    unknown = process_df[process_df.MonthlyIncome.isnull()].as_matrix()\n",
    "    # X为特征属性值\n",
    "    X = known[:, 1:]\n",
    "    # y为结果标签值\n",
    "    y = known[:, 0]\n",
    "    # fit到RandomForestRegressor之中\n",
    "    rfr = RandomForestRegressor(random_state=0, n_estimators=200, max_depth=3, n_jobs=-1)\n",
    "    rfr.fit(X, y)\n",
    "    # 用得到的模型进行未知特征值预测\n",
    "    predicted = rfr.predict(unknown[:, 1:]).round(0)\n",
    "    print(\"预测值： \", predicted)\n",
    "    # 用得到的预测结果填补原缺失数据\n",
    "    df.loc[(df.MonthlyIncome.isnull()), 'MonthlyIncome'] = predicted\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测值：  [ 8311.  1159.  8311. ...,  1159.  2554.  2554.]\n"
     ]
    }
   ],
   "source": [
    "df = set_missing(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.dropna() # 删除缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.drop_duplicates() # 删除重复值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 异常值处理：\n",
    "    异常值：偏离大多数抽样数据的数值，通常指测定值中与平均值的偏差超过两倍标准差的测定值\n",
    "    \n",
    "    通常采用离群值检测的方法对异常值进行检测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**RevolvingUtilizationOfUnsecuredLines**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xda0f438>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAD8CAYAAACcjGjIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzCzH4WBmZjn/H4vB2Jf7vX0mAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc065f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"RevolvingUtilizationOfUnsecuredLines\"].plot(kind=\"box\", grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 使用2来代替大于2的值\n",
    "revNew = []\n",
    "for val in df.RevolvingUtilizationOfUnsecuredLines:\n",
    "    if val <= 2:\n",
    "        revNew.append(val)\n",
    "    else:\n",
    "        revNew.append(2.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.RevolvingUtilizationOfUnsecuredLines = revNew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc0650b8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nveQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd06b780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"RevolvingUtilizationOfUnsecuredLines\"].plot(kind=\"box\",grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**age**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf7bf630>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd924be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.age.plot.box(grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 发现 age 属性中存在0值情况，而这些数据明显是异常值，因此对其进行处理\n",
    "df = df[df[\"age\"] > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NumberOfTime30-59DaysPastDueNotWorse**\n",
    "\n",
    "**NumberOfTime60-89DaysPastDueNotWorse**\n",
    "\n",
    "**NumberOfTimes90DaysLate**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf873f98>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAFpCAYAAACLRc9GAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIqKWBI6IiKglgSMiImr5/y0sd1d9pMNvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf182710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column=[\"NumberOfTime30-59DaysPastDueNotWorse\", \"NumberOfTime60-89DaysPastDueNotWorse\", \"NumberOfTimes90DaysLate\"], \n",
    "            rot=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面的箱线图可以看出 NumberOfTime30-59DaysPastDueNotWorse，NumberOfTime60-89DaysPastDueNotWorse，NumberOfTimes90DaysLate三个特征都存在两个异常值，下面使用 unique() 方法查看具体的异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberOfTime30-59DaysPastDueNotWorse: [ 2  0  1  3  4  5  7 10  6 98 12  8  9 96 13 11]\n",
      "NumberOfTime60-89DaysPastDueNotWorse: [ 0  1  2  5  3 98  4  6  7  8 96 11  9]\n",
      "NumberOfTimes90DaysLate: [ 0  1  3  2  5  4 98 10  9  6  7  8 15 96 11 13 14 17 12]\n"
     ]
    }
   ],
   "source": [
    "print(\"NumberOfTime30-59DaysPastDueNotWorse:\", df[\"NumberOfTime30-59DaysPastDueNotWorse\"].unique())\n",
    "print(\"NumberOfTime60-89DaysPastDueNotWorse:\", df[\"NumberOfTime60-89DaysPastDueNotWorse\"].unique())\n",
    "print(\"NumberOfTimes90DaysLate:\", df[\"NumberOfTimes90DaysLate\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def replaceOutlier(data):\n",
    "    New = []\n",
    "    med = data.median()\n",
    "    for val in data:\n",
    "        if ((val == 98) | (val == 96)):\n",
    "            New.append(med)\n",
    "        else:\n",
    "            New.append(val)\n",
    "    return New\n",
    "\n",
    "df[\"NumberOfTime30-59DaysPastDueNotWorse\"] = replaceOutlier(df[\"NumberOfTime30-59DaysPastDueNotWorse\"])\n",
    "df[\"NumberOfTime60-89DaysPastDueNotWorse\"] = replaceOutlier(df[\"NumberOfTime60-89DaysPastDueNotWorse\"])\n",
    "df[\"NumberOfTimes90DaysLate\"] = replaceOutlier(df[\"NumberOfTimes90DaysLate\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAFpCAYAAACLRc9GAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nceIwM7OCOHGYmVlBnDjMzKwg/x+FE57FbYM9DwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf87f6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 下图可以看出 NumberOfTime30-59DaysPastDueNotWorse 删除 98 和 96 两个异常值时，\n",
    "# NumberOfTime60-89DaysPastDueNotWorse 和 NumberOfTimes90DaysLate 的两个异常值也同时删除了\n",
    "df.boxplot(column=[\"NumberOfTime30-59DaysPastDueNotWorse\", \"NumberOfTime60-89DaysPastDueNotWorse\", \"NumberOfTimes90DaysLate\"], \n",
    "            rot=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DebtRatio**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf2da2e8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAD8CAYAAACLrvgBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdce3be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"DebtRatio\"].plot(kind=\"box\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 使用中位数绝对偏差 MAD（median absolute deviation）方法进行异常值的检测\n",
    "from scipy.stats import norm\n",
    "\n",
    "def mad_based_outlier(points, thresh=3.5):\n",
    "    if type(points) is list:\n",
    "        points = np.asarray(points)\n",
    "    if len(points.shape) == 1:\n",
    "        points = points[:, None]\n",
    "    med = np.median(points, axis=0)\n",
    "    abs_dev = np.absolute(points - med)\n",
    "    med_abs_dev = np.median(abs_dev)\n",
    "\n",
    "    mod_z_score = norm.ppf(0.75) * abs_dev / med_abs_dev\n",
    "    return mod_z_score > thresh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 检测出最小的异常值,用于替换异常值\n",
    "minUpperBound = min([val for (val, out) in zip(df.DebtRatio, mad_based_outlier(df.DebtRatio)) if out == True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "newDebtRatio = []\n",
    "for val in df.DebtRatio:\n",
    "    if val > minUpperBound:\n",
    "        newDebtRatio.append(minUpperBound)\n",
    "    else:\n",
    "        newDebtRatio.append(val)\n",
    "\n",
    "df.DebtRatio = newDebtRatio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    145562.000000\n",
       "mean          0.571777\n",
       "std           0.546250\n",
       "min           0.000000\n",
       "25%           0.173933\n",
       "50%           0.359088\n",
       "75%           0.770648\n",
       "max           1.566521\n",
       "Name: DebtRatio, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.DebtRatio.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd189940>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAEOFJREFUeJzt3X2MpWV5x/HvzwWEIkLr4qRlkaHt\nkrriWz2CTW0cipoFE6gNtmxtLQ06TSv6h7VxaxuwmDQoaW2aQunEUqyxUGt9WVlcaC2ntAqWRV7k\nRcwGUSY0XQVLXURh8eofc9aOw8yeMzPnzDD3fj/JJPOc5zr3fZ3NM799cs85c6eqkCS15Rmr3YAk\nafgMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDlqtidevX1/j4+OrNb20X48+\n+iiHH374archPcUtt9zyzao6ul/dqoX7+Pg4O3fuXK3ppf3qdrtMTEysdhvSUyT52iB1fZdlklye\nZHeSO/dTM5HktiR3Jfm3xTQqSRq+QdbcrwA2L3QyyVHApcAZVfUC4A3DaU2StFR9w72qbgAe3k/J\nrwEfr6qv9+p3D6k3SdISDePdMicAP5qkm+SWJG8awpiSpGUYxi9UDwJeBpwKHAbcmOSmqvrK3MIk\nk8AkwNjYGN1udwjTS8O3Z88er0+tacMI92ngm1X1KPBokhuAFwNPCfeqmgKmADqdTvluBD1d+W4Z\nrXXDWJb5FPALSQ5K8iPAycA9QxhXkrREfe/ck1wJTADrk0wDFwAHA1TVZVV1T5IdwB3A94EPVtWC\nb5uUJI1e33Cvqi0D1FwMXDyUjqQhS7Ii87gfsZ5O/Nsyal5VLfrruHddvejnSE8nhrskNchwl6QG\nGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDh\nLkkNMtwlqUF9wz3J5Ul2J9nv1nlJXp7kySRnDa89SdJSDHLnfgWweX8FSdYB7wOuHUJPkqRl6hvu\nVXUD8HCfsrcB/wTsHkZTkqTl6btBdj9JjgFeD/wi8PI+tZPAJMDY2Bjdbne500sj4/WptWzZ4Q78\nOfCuqnqy3y7zVTUFTAF0Op2amJgYwvTSCOzYjten1rJhhHsHuKoX7OuB05PsrapPDmFsSdISLDvc\nq+r4fd8nuQK42mCXpNXVN9yTXAlMAOuTTAMXAAcDVNVlI+1OkrQkfcO9qrYMOlhVnbOsbiRJQ+En\nVCWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNd\nkhpkuEtSgwx3SWqQ4S5JDeob7kkuT7I7yZ0LnH9jkjt6X59P8uLhtylJWoxB7tyvADbv5/xXgVdV\n1YuA9wJTQ+hLkrQMg2yzd0OS8f2c//ysw5uADctvS5K0HH3DfZHOBT6z0Mkkk8AkwNjYGN1ud8jT\nS8Pj9am1bGjhnuQUZsL9lQvVVNUUvWWbTqdTExMTw5peGq4d2/H61Fo2lHBP8iLgg8BpVfXQMMaU\nJC3dst8KmeR5wMeB36iqryy/JUnScvW9c09yJTABrE8yDVwAHAxQVZcB5wPPAS5NArC3qjqjaliS\n1N8g75bZ0uf8m4E3D60jSdKy+QlVSWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMM\nd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KD+oZ7ksuT7E5y5wLnk+QvkuxKckeS\nnx1+m5KkxRjkzv0KYPN+zp8GbOx9TQJ/tfy2JEnL0Tfcq+oG4OH9lJwJ/F3NuAk4KsmPD6tBSdLi\nDWPN/RjggVnH073HJEmrpO8G2QPIPI/VvIXJJDNLN4yNjdHtdocwvTQaXp9ay4YR7tPAsbOONwAP\nzldYVVPAFECn06mJiYkhTC+NwI7teH1qLRvGssw24E29d828Anikqv5rCONKkpao7517kiuBCWB9\nkmngAuBggKq6DLgGOB3YBXwH+K1RNStJGkzfcK+qLX3OF/DWoXUkSVo2P6EqSQ0y3CWpQYa7JDXI\ncJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3\nSWrQQOGeZHOSe5PsSrJ1nvPPS3J9kluT3JHk9OG3KkkaVN9wT7IOuAQ4DdgEbEmyaU7ZHwEfraqX\nAmcDlw67UUnS4Aa5cz8J2FVV91XV48BVwJlzagp4du/7I4EHh9eiJGmx+u6hChwDPDDreBo4eU7N\ne4DrkrwNOBx49VC6kyQtySDhnnkeqznHW4ArqupPk/wc8OEkJ1bV939ooGQSmAQYGxuj2+0uoWVp\nZXh9ai0bJNyngWNnHW/gqcsu5wKbAarqxiSHAuuB3bOLqmoKmALodDo1MTGxtK6lUduxHa9PrWWD\nrLnfDGxMcnySQ5j5hem2OTVfB04FSPJ84FDgG8NsVJI0uL7hXlV7gfOAa4F7mHlXzF1JLkxyRq/s\n94C3JLkduBI4p6rmLt1IklZIViuDO51O7dy5c1Xm1tr14j++jkcee2K12xiKIw87mNsveO1qt6E1\nJsktVdXpVzfImrv0tPHIY09w/0WvG/k83W535Gvu41u3j3R8Hdj88wOS1CDDXZIaZLhLUoMMd0lq\nkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYN\nFO5JNie5N8muJFsXqPmVJHcnuSvJ3w+3TUnSYvTdiSnJOuAS4DXANHBzkm1Vdfesmo3AHwA/X1Xf\nSvLcUTUsSepvkDv3k4BdVXVfVT0OXAWcOafmLcAlVfUtgKraPdw2JUmLMcgeqscAD8w6ngZOnlNz\nAkCSzwHrgPdU1Y65AyWZBCYBxsbG6Ha7S2hZB7qVuG727NmzIvP4M6BRGSTcM89jNc84G4EJYAPw\n70lOrKr/+aEnVU0BUwCdTqdGvQGxGrRj+8g3roaV2SB7pV6LDkyDLMtMA8fOOt4APDhPzaeq6omq\n+ipwLzNhL0laBYOE+83AxiTHJzkEOBvYNqfmk8ApAEnWM7NMc98wG5UkDa5vuFfVXuA84FrgHuCj\nVXVXkguTnNEruxZ4KMndwPXA71fVQ6NqWpK0f4OsuVNV1wDXzHns/FnfF/CO3pckaZX5CVVJapDh\nLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S\n1CDDXZIaZLhLUoMGCvckm5Pcm2RXkq37qTsrSSXpDK9FSdJi9Q33JOuAS4DTgE3AliSb5qk7Ang7\n8IVhNylJWpxB7txPAnZV1X1V9ThwFXDmPHXvBd4PfHeI/UmSlmCQPVSPAR6YdTwNnDy7IMlLgWOr\n6uok71xooCSTwCTA2NgY3W530Q1LK3Hd7NmzZ0Xm8WdAozJIuGeex+oHJ5NnAB8Azuk3UFVNAVMA\nnU6nJiYmBmpS+oEd21mJ66bb7Y5+nhV6LTowDbIsMw0cO+t4A/DgrOMjgBOBbpL7gVcA2/ylqiSt\nnkHC/WZgY5LjkxwCnA1s23eyqh6pqvVVNV5V48BNwBlVtXMkHUuS+uob7lW1FzgPuBa4B/hoVd2V\n5MIkZ4y6QUnS4g2y5k5VXQNcM+ex8xeonVh+W5Kk5Rgo3KWniyOev5UXfmjBz9EN14dGO/wRzwd4\n3Wgn0QHLcNea8u17LuL+i0YfiCvxbpnxrdtHOr4ObP5tGUlqkOEuSQ0y3CWpQYa7JDXIcJekBhnu\nktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUoIHCPcnmJPcm2ZXkKX9MO8k7\nktyd5I4kn01y3PBblSQNqm+4J1kHXAKcBmwCtiTZNKfsVqBTVS8CPga8f9iNSpIGN8id+0nArqq6\nr6oeB64CzpxdUFXXV9V3eoc3ARuG26YkaTEGCfdjgAdmHU/3HlvIucBnltOUJGl5BtlmL/M8VvMW\nJr8OdIBXLXB+EpgEGBsbo9vtDtalNMtKXDd79uxZkXn8GdCoDBLu08Cxs443AA/OLUryauAPgVdV\n1ffmG6iqpoApgE6nU6Peo1IN2rF95HubwsrsobpSr0UHpkGWZW4GNiY5PskhwNnAttkFSV4K/DVw\nRlXtHn6bkqTF6HvnXlV7k5wHXAusAy6vqruSXAjsrKptwMXAs4B/TALw9ao6Y4R96wA2vnX7yky0\nY7TzHHnYwSMdXwe2VM27fD5ynU6ndu7cuSpzS/2Mb93O/Re9brXbkJ4iyS1V1elX5ydUJalBhrsk\nNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KD\nDHdJapDhLkkNGijck2xOcm+SXUm2znP+mUn+oXf+C0nGh92oJGlwfcM9yTrgEuA0YBOwJcmmOWXn\nAt+qqp8GPgC8b9iNSpIGN8id+0nArqq6r6oeB64CzpxTcybwod73HwNOTW8zVUnSyhsk3I8BHph1\nPN17bN6aqtoLPAI8ZxgNSpIW76ABaua7A5+7q/YgNSSZBCYBxsbG6Ha7A0wvLc8pp5yypOdlkYuL\n119//ZLmkUZhkHCfBo6ddbwBeHCBmukkBwFHAg/PHaiqpoApgE6nUxMTE0toWVqcqqfcZ/TV7Xbx\n+tRaNsiyzM3AxiTHJzkEOBvYNqdmG/Cbve/PAv61lvITJUkair537lW1N8l5wLXAOuDyqroryYXA\nzqraBvwN8OEku5i5Yz97lE1LkvZvkGUZquoa4Jo5j50/6/vvAm8YbmuSpKXyE6qS1CDDXZIaZLhL\nUoMMd0lqkOEuSQ3Kar0dPck3gK+tyuRSf+uBb652E9I8jquqo/sVrVq4S09nSXZWVWe1+5CWymUZ\nSWqQ4S5JDTLcpflNrXYD0nK45i5JDfLOXZIaZLhrTUryZJLbktyV5PYk70iy3+s5yUSSqxc49+4F\nxr8zyaeTHNVn7KOS/O6s459I8rHFvCZpmAx3rVWPVdVLquoFwGuA04ELljHeu+cc7xv/RGb+jPVb\n+zz/KOAH4V5VD1bVWcvoR1oWw11rXlXtZmb7xvMyY12Si5PcnOSOJL89q/zZST6R5O4klyV5RpKL\ngMN6d+ofmWeKG+ntG5zkWUk+m+SLSb6UZN9m8RcBP9Ub4+Ik40nu7D3n0CR/26u/NcnS9v2TFmGg\nv+cuPd1V1X29ZZnnAmcCj1TVy5M8E/hckut6pScBm5j5dPQO4JeramuS86rqJXPHTbIOOJWZDWkA\nvgu8vqr+N8l64KYk24CtwIn7xkgyPmuYt/Z6fGGSnwGuS3JCbx8EaSS8c1dL9m3U/lrgTUluA74A\nPAfY2Dv3n1V1X1U9CVwJvHKBsQ7rPf8h4MeAf541x58kuQP4F2bu6Mf69PVK4MMAVfVlZv5jOWGR\nr01aFMNdTUjyk8CTwG5mAvhtvTXzl1TV8VW178597nt/F3ov8GO9u/DjgEP4/zX3NwJHAy/rnf9v\n4NB+7S3u1UjLZ7hrzUtyNHAZ8Je9jdmvBX4nycG98yckObxXflJvs/dnAL8K/Efv8Sf21c9WVY8A\nbwfe2Tt/JLC7qp7orZ0f1yv9NnDEAi3ewMx/CiQ5AXgecO+yXrTUh2vuWqv2LZscDOxlZtnjz3rn\nPgiMA19MEuAbwC/1zt3IzC8/X8hM6H6i9/gUcEeSL1bVG2dPVFW3JrmdmY3fPwJ8OslO4Dbgy72a\nh5J8rvdL1M8Al8wa4lLgsiRf6vV6TlV9bzj/DNL8/ISqJDXIZRlJapDhLkkNMtwlqUGGuyQ1yHCX\npAYZ7pLUIMNdkhpkuEtSg/4PkdSHpCEeVcEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf349d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"DebtRatio\"].plot.box(grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Monthly income**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.455620e+05\n",
       "mean     6.116310e+03\n",
       "std      1.318226e+04\n",
       "min      0.000000e+00\n",
       "25%      2.554000e+03\n",
       "50%      5.000000e+03\n",
       "75%      8.166000e+03\n",
       "max      3.008750e+06\n",
       "Name: MonthlyIncome, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.MonthlyIncome.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x102bb9e8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAD8CAYAAACyyUlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAGqlJREFUeJzt3X9wXeV95/H3x5INjB1ifiSKa5uY\nNs5URptCUAi70exI9taYpDNOZ5MBpVs7sWbVbYElgwdCoj+ApGKIdwslOGHHXSmBTLjA0mZxs3ip\n1+hO1g2/TCEgEFmrJASBAwXbBNmOjeXv/nEfkWvlWtLVCT668uc1c+ee+z3PeZ5HRvjj8+Oeo4jA\nzMwsi1l5T8DMzGqfw8TMzDJzmJiZWWYOEzMzy8xhYmZmmTlMzMwsM4eJmZll5jAxM7PMHCZmZpZZ\nfd4TOF7OPPPMWLJkSd7TMKto3759zJ07N+9pmP2GJ5544vWIeN9E7U6YMFmyZAk7duzIexpmFRWL\nRVpbW/OehtlvkPTiZNr5MJeZmWXmMDEzs8wcJmZmlpnDxMzMMnOYmJlZZhOGiaSTJT0m6ceSnpV0\nQ6qfLelRSTsl3SNpTqqflD4PpvVLyvr6cqr/RNJFZfVVqTYo6dqyetVjmNWSQqFAU1MTK1asoKmp\niUKhkPeUzKZkMpcGHwSWR8SwpNnAdklbgKuAWyLibkn/DegAbk/veyLiQ5IuBb4OXCJpGXApcA7w\nO8D/kfThNMY3gT8EhoDHJW2OiOfStpMe47fw52F23BQKBbq6uujp6WFkZIS6ujo6OjoAaG9vz3l2\nZtWZcM8kSobTx9npFcBy4L5UvwP4dFpenT6T1q+QpFS/OyIORsRPgUHggvQajIgXIuIQcDewOm1T\n7RhmNaO7u5uenh7a2tqor6+nra2Nnp4euru7856aWdUmdc5EUp2kp4DXgK3APwN7I+JwajIELEzL\nC4GXANL6N4EzyutjtjlW/YwpjGFWMwYGBmhpaTmq1tLSwsDAQE4zMpu6SX0DPiJGgHMlzQe+DzRW\napbeK+0hxDj1SoE2XvvxxjiKpE6gE6ChoYFisVhhM7N8nHXWWWzcuJHzzjuP4eFhisUiTz75JGed\ndZZ/V63mVHU7lYjYK6kIXAjMl1Sf9gwWAa+kZkPAYmBIUj3wXmB3WX1U+TaV6q9PYYyx890EbAJo\nbm4O367CppMbb7zxnXMmJ598MhHBbbfdxo033uhbq1jNmTBMJL0PeDsFySnAv6N0wrsP+Aylcxxr\ngfvTJpvT54fT+ociIiRtBu6SdDOlE/BLgcco7WUslXQ28DKlk/SfS9tUNUamPwmz42z0JPsVV1zB\nwMAAjY2NdHd3++S71SRN9HewpI9QOtldR+mQ1L0R8VVJv0vpL/nTgSeB/xARByWdDHwXOI/S3sKl\nEfFC6qsLWAccBr4YEVtS/ZPAX6cxeiOiO9WrHuNYmpubwzd6tOnKN3q06UrSExHRPGG7E+Uf9A4T\nm84cJjZdTTZM/A14MzPLzGFiZmaZOUzMzCwzh4mZmWXmMDEzs8wcJmZmlpnDxMzMMnOYmJlZZg4T\nMzPLzGFiZmaZOUzMzCwzh4mZmWXmMDEzs8wcJmZmlpnDxMzMMnOYmJlZZg4TMzPLzGFiZmaZOUzM\nzCwzh4mZmWXmMDEzs8wcJmZmlpnDxMzMMnOYmJlZZhOGiaTFkvokDUh6VtKVqX69pJclPZVenyzb\n5suSBiX9RNJFZfVVqTYo6dqy+tmSHpW0U9I9kuak+knp82Bav2SiMczM7PibzJ7JYWB9RDQCFwKX\nSVqW1t0SEeem1wMAad2lwDnAKuBbkuok1QHfBC4GlgHtZf18PfW1FNgDdKR6B7AnIj4E3JLaHXOM\nKf8pmJlZJhOGSUTsioh/SstvAQPAwnE2WQ3cHREHI+KnwCBwQXoNRsQLEXEIuBtYLUnAcuC+tP0d\nwKfL+rojLd8HrEjtjzWGmZnloKpzJukw03nAo6l0uaSnJfVKOi3VFgIvlW02lGrHqp8B7I2Iw2Pq\nR/WV1r+Z2h+rLzMzy0H9ZBtKmgf8LfDFiPilpNuBrwGR3v8KWAeowuZB5eCKcdozzrrxtimfcyfQ\nCdDQ0ECxWKywmVn+hoeH/ftpNW1SYSJpNqUg+V5E/B1ARLxatv5vgB+kj0PA4rLNFwGvpOVK9deB\n+ZLq095HefvRvoYk1QPvBXZPMMY7ImITsAmgubk5WltbJ/Pjmh13xWIR/35aLZvM1VwCeoCBiLi5\nrL6grNkfA/1peTNwaboS62xgKfAY8DiwNF25NYfSCfTNERFAH/CZtP1a4P6yvtam5c8AD6X2xxrD\nzMxyMJk9k08Afwo8I+mpVPsKpauxzqV0eOlnwJ8BRMSzku4FnqN0JdhlETECIOly4EGgDuiNiGdT\nf18C7pb0l8CTlMKL9P5dSYOU9kgunWgMMzM7/lT6h/7M19zcHDt27Mh7GmYV+TCXTVeSnoiI5ona\n+RvwZmaWmcPEzMwyc5iYmVlmDhMzM8vMYWJmZpk5TMzMLDOHiZmZZeYwMTOzzBwmZmaWmcPEzMwy\nc5iYmVlmDhMzM8vMYWJmZpk5TMzMLDOHiZmZZeYwMTOzzBwmZmaWmcPEzMwyc5iYmVlmDhMzM8vM\nYWJmZpk5TMzMLDOHiZmZZeYwMTOzzCYME0mLJfVJGpD0rKQrU/10SVsl7Uzvp6W6JH1D0qCkpyV9\ntKyvtan9Tklry+rnS3ombfMNSZrqGGZmdvxNZs/kMLA+IhqBC4HLJC0DrgW2RcRSYFv6DHAxsDS9\nOoHboRQMwHXAx4ELgOtGwyG16SzbblWqVzWGmZnlY8IwiYhdEfFPafktYABYCKwG7kjN7gA+nZZX\nA3dGySPAfEkLgIuArRGxOyL2AFuBVWndqRHxcEQEcOeYvqoZw8zMclDVORNJS4DzgEeBhojYBaXA\nAd6fmi0EXirbbCjVxqsPVagzhTHMzCwH9ZNtKGke8LfAFyPil+m0RsWmFWoxhfq405nMNpI6KR0G\no6GhgWKxOEG3ZvkYHh7276fVtEmFiaTZlILkexHxd6n8qqQFEbErHWJ6LdWHgMVlmy8CXkn11jH1\nYqovqtB+KmMcJSI2AZsAmpubo7W1dWwTs2mhWCzi30+rZZO5mktADzAQETeXrdoMjF6RtRa4v6y+\nJl1xdSHwZjpE9SCwUtJp6cT7SuDBtO4tSRemsdaM6auaMczMLAeT2TP5BPCnwDOSnkq1rwA3AfdK\n6gB+Dnw2rXsA+CQwCOwHvgAQEbslfQ14PLX7akTsTst/DnwHOAXYkl5UO4aZmeVjwjCJiO1UPkcB\nsKJC+wAuO0ZfvUBvhfoOoKlC/Y1qxzAzs+PP34A3M7PMHCZmZpaZw8TMzDJzmJiZWWYOEzMzy8xh\nYpajQqFAU1MTK1asoKmpiUKhkPeUzKZk0rdTMbPfrkKhQFdXFz09PYyMjFBXV0dHRwcA7e3tOc/O\nrDreMzHLSXd3Nz09PbS1tVFfX09bWxs9PT10d3fnPTWzqjlMzHIyMDBAS0vLUbWWlhYGBgZympHZ\n1DlMzHLS2NjI9u3bj6pt376dxsbGnGZkNnUOE7OcdHV10dHRQV9fH4cPH6avr4+Ojg66urrynppZ\n1XwC3iwnoyfZr7jiCgYGBmhsbKS7u9sn360mqXTPxJmvubk5duzYkfc0zCry80xsupL0REQ0T9TO\nh7nMzCwzh4mZmWXmMDEzs8wcJmZmlpnDxMzMMnOYmJlZZg4TMzPLzGFiliPfgt5mCn8D3iwnvgW9\nzSTeMzHLiW9BbzOJw8QsJ74Fvc0kE4aJpF5Jr0nqL6tdL+llSU+l1yfL1n1Z0qCkn0i6qKy+KtUG\nJV1bVj9b0qOSdkq6R9KcVD8pfR5M65dMNIZZLfEt6G0mmcyeyXeAVRXqt0TEuen1AICkZcClwDlp\nm29JqpNUB3wTuBhYBrSntgBfT30tBfYAHaneAeyJiA8Bt6R2xxyjuh/bLH++Bb3NJBOegI+IH5bv\nFUxgNXB3RBwEfippELggrRuMiBcAJN0NrJY0ACwHPpfa3AFcD9ye+ro+1e8DNkrSOGM8PMk5mk0L\nvgW9zSRZrua6XNIaYAewPiL2AAuBR8raDKUawEtj6h8HzgD2RsThCu0Xjm4TEYclvZnajzfGUSR1\nAp0ADQ0NFIvF6n9Ks3fRggUL2LhxI8PDw8ybNw/Av6dWk6YaJrcDXwMivf8VsA5QhbZB5cNpMU57\nxlk33jZHFyM2AZug9DwTPy/Cpis/z8Rq3ZSu5oqIVyNiJCKOAH/Drw9lDQGLy5ouAl4Zp/46MF9S\n/Zj6UX2l9e8Fdo/Tl5mZ5WRKYSJpQdnHPwZGr/TaDFyarsQ6G1gKPAY8DixNV27NoXQCfXOUHvPY\nB3wmbb8WuL+sr7Vp+TPAQ6n9scYwM7OcTHiYS1IBaAXOlDQEXAe0SjqX0uGlnwF/BhARz0q6F3gO\nOAxcFhEjqZ/LgQeBOqA3Ip5NQ3wJuFvSXwJPAj2p3gN8N51g300pgMYdw8zM8uFnwJtNAz5nYtOV\nnwFvZmbHjcPEzMwyc5iYmVlmDhMzM8vMYWJmZpk5TMzMLDOHiZmZZeYwMTOzzBwmZmaWmcPEzMwy\nc5iYmVlmDhMzM8vMYWJmZpk5TMzMLDOHiZmZZeYwMTOzzBwmZmaWmcPEzMwyc5iYmVlmDhOzHBUK\nBZqamlixYgVNTU0UCoW8p2Q2JfV5T8DsRFUoFOjq6qKnp4eRkRHq6uro6OgAoL29PefZmVXHeyZm\nOenu7qanp4e2tjbq6+tpa2ujp6eH7u7uvKdmVjWHiVlOBgYGaGlpOarW0tLCwMBATjMymzqHiVlO\nGhsb2b59+1G17du309jYmNOMzKZuwjCR1CvpNUn9ZbXTJW2VtDO9n5bqkvQNSYOSnpb00bJt1qb2\nOyWtLaufL+mZtM03JGmqY5jVkq6uLjo6Oujr6+Pw4cP09fXR0dFBV1dX3lMzq15EjPsC/i3wUaC/\nrLYBuDYtXwt8PS1/EtgCCLgQeDTVTwdeSO+npeXT0rrHgH+dttkCXDyVMSZ6nX/++WE23dx1111x\nzjnnxKxZs+Kcc86Ju+66K+8pmR0F2BGT+Dt2wj2TiPghsHtMeTVwR1q+A/h0Wf3ONIdHgPmSFgAX\nAVsjYndE7AG2AqvSulMj4uE06TvH9FXNGGY1p729nf7+frZt20Z/f7+v4rKaNdVLgxsiYhdAROyS\n9P5UXwi8VNZuKNXGqw9VqE9ljF1jJympE+gEaGhooFgsVvdTmh0nw8PD/v20mvbb/p6JKtRiCvWp\njPGbxYhNwCaA5ubmaG1tnaBrs3wUi0X8+2m1bKpXc706emgpvb+W6kPA4rJ2i4BXJqgvqlCfyhhm\nZpaTqYbJZmD0iqy1wP1l9TXpiqsLgTfToaoHgZWSTktXZa0EHkzr3pJ0YbqKa82YvqoZw8zMcjLh\nYS5JBaAVOFPSEHAdcBNwr6QO4OfAZ1PzByhdbTUI7Ae+ABARuyV9DXg8tftqRIye1P9z4DvAKZSu\n0tqS6lWNYWZm+ZkwTCLiWJeXrKjQNoDLjtFPL9Bbob4DaKpQf6PaMczMLB/+BryZmWXmMDEzs8wc\nJmY58vNMbKbw80zMcuLnmdhM4j0Ts5z4eSY2kzhMzHLi55nYTOIwMcuJn2diM4nDxCwnfp6JzSQ+\nAW+Wk9GT7FdccQUDAwM0NjbS3d3tk+9Wk7xnYmZmmXnPxCwnvjTYZhKVbnU18zU3N8eOHTvynobZ\nO5qamli6dClbtmzh4MGDnHTSSVx88cXs3LmT/v7+vKdnBoCkJyKieaJ23jMxy8lzzz3H888/z4YN\nG1i2bBnPPfcc11xzDUeOHMl7amZV8zkTsxx1dnZy1VVXcfLJJ3PVVVfR2dmZ95TMpsR7JmY5iQju\nu+8+tmzZwosvvsgHP/hB9u3bx4ly6NlmFu+ZmOWkvr6e/fv3A1B60Cjs37+f+nr/G89qj8PELCen\nnnoq+/fv58CBAxw5coQDBw6wf/9+Tj311LynZlY1h4lZTvbs2cPcuXPZvbv0BOvdu3czd+5c9uzZ\nk/PMzKrnMDHLyZw5c7jhhhs4dOgQfX19HDp0iBtuuIE5c+bkPTWzqvl7JmY5mTVrFmeeeSZz5849\n6gT866+/7suDbdrw90zMprmFCxfyxhtvsHfvXiKCl19+mfr6ehYuXJj31Myq5sNcZjnZv38/hw4d\n4qabbmLLli3cdNNNHDp06J0rvMxqicPELCe7d+/m6quvpre3l0996lP09vZy9dVXv3NC3qyWZAoT\nST+T9IykpyTtSLXTJW2VtDO9n5bqkvQNSYOSnpb00bJ+1qb2OyWtLaufn/ofTNtqvDHMas3y5cvp\n7+9n27Zt9Pf3s3z58rynZDYlv409k7aIOLfsBM21wLaIWApsS58BLgaWplcncDuUggG4Dvg4cAFw\nXVk43J7ajm63aoIxzGrGokWLWLNmzVEPx1qzZg2LFi3Ke2pmVXs3DnOtBu5Iy3cAny6r3xkljwDz\nJS0ALgK2RsTuiNgDbAVWpXWnRsTDUbrk7M4xfVUaw6xmbNiwgZGREdatW8fKlStZt24dIyMjbNiw\nIe+pmVUta5gE8A+SnpA0eoe6hojYBZDe35/qC4GXyrYdSrXx6kMV6uONYVYz2tvbufXWW5k7dy6S\nmDt3LrfeequfZWI1KeulwZ+IiFckvR/YKun5cdqqQi2mUJ+0FHCdAA0NDRSLxWo2N3vXLViwgI0b\nNzI8PMy8efMA/HtqNSlTmETEK+n9NUnfp3TO41VJCyJiVzpU9VpqPgQsLtt8EfBKqreOqRdTfVGF\n9owzxtj5bQI2QelLi62trZWameWmUCjQ3d39zjPgu7q6vGdiNWnKh7kkzZX0ntFlYCXQD2wGRq/I\nWgvcn5Y3A2vSVV0XAm+mQ1QPAislnZZOvK8EHkzr3pJ0YbqKa82YviqNYVYzCoUCV155Jfv27QNg\n3759XHnllRQKhZxnZla9Kd9ORdLvAt9PH+uBuyKiW9IZwL3AWcDPgc9GxO4UCBspXZG1H/hCRIxe\nTrwO+Erqqzsivp3qzcB3gFOALcAVERHHGmO8+fp2KjbdLF68mOHhYebPn//O7VT27t3LvHnzeOml\nlybuwOw4mOztVHxvLrOcSKKhoYFCocDIyAh1dXW0t7fz6quv+gFZNm1MNkz8DXizHK1fv562tjbq\n6+tpa2tj/fr1eU/JbEp8o0ezHN188800NzczMjJCX18fN998c95TMpsSh4lZThYtWsTw8DDr1q17\n55zJr371K38D3mqSD3OZ5WTDhg3Mnj0b+PUz4GfPnu1vwFtNcpiY5aS9vZ1LLrmEXbt2ceTIEXbt\n2sUll1zi75lYTXKYmOWkUChwzz33sGDBAmbNmsWCBQu45557/D0Tq0kOE7OcXHPNNbz99tsA71wK\n/Pbbb3PNNdfkOS2zKfEJeLOcDA0N0dDQQG9v71HfMxkaGpp4Y7NpxmFiliNJRz0Q6wMf+ECOszGb\nOh/mMsvRL37xi3E/m9UKh4lZzkYvCx59N6tFDhOznI2efPf9uKyWOUzMclZXV3fUu1ktcpiY5WzO\nnDlIYs6cOXlPxWzKfDWXWc4OHDhw1LtZLfKeiZmZZeYwMTOzzBwmZmaWmcPEzMwyc5iYmVlmDhMz\nM8vMYWJmZpk5TMzMLDOHiZmZZVbTYSJplaSfSBqUdG3e8zEzO1HVbJhIqgO+CVwMLAPaJS3Ld1Zm\nZiemmg0T4AJgMCJeiIhDwN3A6pznZGZ2QqrlGz0uBF4q+zwEfDynudgM9Qc3/ANvHni7qm1e/Pof\nZR53Mg/K+uCXflBVn+89ZTY/vm7lVKdkNq5aDpNK/7cd9XQhSZ1AJ0BDQwPFYvE4TMtmkiNL1vOe\nKrdp+k7TuzKX31TdacIjQLF427szFTvh1XKYDAGLyz4vAl4pbxARm4BNAM3NzdHa2nrcJmczwzM8\n8671Pd7eh5+6aLWmls+ZPA4slXS2pDnApcDmnOdkNmnHCgwHidWimg2TiDgMXA48CAwA90bEs/nO\nyqw6EUFE0NfX986yWS2q5cNcRMQDwAN5z8PM7ERXs3smZmY2fThMzMwsM4eJmZll5jAxM7PMHCZm\nZpaZTpRLESX9C/Bi3vMwO4YzgdfznoRZBR+MiPdN1OiECROz6UzSjohoznseZlPlw1xmZpaZw8TM\nzDJzmJhND5vynoBZFj5nYmZmmXnPxMzMMnOY2AlFUkj6btnnekn/Iqm6xxb+evv5kv6i7HPrsfqS\nVJQ07hVbkoanMg+zvDlM7ESzD2iSdEr6/IfAyxn6mw/8xYStzGY4h4mdiLYAn0rL7UBhdIWk0yX9\nT0lPS3pE0kdS/XpJvWnv4gVJ/zltchPwe5KekvRfUm2epPskPS/pexrzSEVJHZJuKfv8HyXdPKZN\naxrrN/qR9DFJP5L0Y0mPSXqPpJMlfVvSM5KelNSW2n4+/Tx/L+mnki6XdFVq84ik01O735P0vyU9\nIen/Svr939Yftp0gRh/I45dfJ8ILGAY+AtwHnAw8BbQCP0jrbwOuS8vLgafS8vXAj4CTKH1b/Q1g\nNrAE6C/rvxV4k9JjpGcBDwMtaV0RaAbmAv8MzE71HwH/anR+4/UDzAFeAD6W2p1K6blE64Fvp9rv\nAz9PP9/ngUHgPcD7Up//KbW7BfhiWt4GLE3LHwceyvu/lV+19arph2OZTUVEPC1pCaW9krEPV2sB\n/n1q95CkMyS9N637XxFxEDgo6TWg4RhDPBYRQwCSnqIUONvLxt8n6SHgjyQNUAqVSg+br9TPm8Cu\niHg89fXLtL6FUhASEc9LehH4cOqnLyLeAt6S9Cbw96n+DPARSfOAfwP8j7KdqJOO8bOZVeQwsRPV\nZuC/UtoDOKOsrgptR6+fP1hWG+HY//9Mpt1/B74CPA98u4p+VDafcpXmXamfI2Wfj6Q+ZwF7I+Lc\ncfowG5fPmdiJqhf4aoU9gh8CfwKl8xbA66P/+j+GtygdQqpKRDwKLAY+R9k5m0l4HvgdSR9Lc3yP\npPox8/4wcBbwk0nO5ZfATyV9Nm0vSX9QxZzMHCZ2YoqIoYi4tcKq64FmSU9TOrm+doJ+3gD+UVJ/\n2Qn4yboX+MeI2DPZDSLiEHAJcJukHwNbKZ0b+RZQJ+kZ4B7g8+mQ3GT9CdCR+nwWWF3Ftmb+BrxZ\nXtL3UW6JiG15z8UsK++ZmB1n6YuO/w844CCxmcJ7JmZmlpn3TMzMLDOHiZmZZeYwMTOzzBwmZmaW\nmcPEzMwyc5iYmVlm/x/hYyExzJAz6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf81feb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.MonthlyIncome.plot.box(grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "minUpperBound_MonthlyIncome = min([val for (val, out) in zip(df.MonthlyIncome, mad_based_outlier(df.MonthlyIncome)) if out == True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "newMonthlyIncome = []\n",
    "for val in df.MonthlyIncome:\n",
    "    if val > minUpperBound_MonthlyIncome:\n",
    "        newMonthlyIncome.append(minUpperBound_MonthlyIncome)\n",
    "    else:\n",
    "        newMonthlyIncome.append(val)\n",
    "\n",
    "df.MonthlyIncome = newMonthlyIncome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf80c898>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAD8CAYAAACcjGjIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf81f6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.MonthlyIncome.plot.box()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NumberOfOpenCreditLinesAndLoans**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110370f0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAGLxJREFUeJzt3X+cVPV97/HXe9dl+aEGrCsalCwB\noShBEjc+NOTWCP7IDYi2N5LYTUqvKKWP1hqvVrjY3jZtQ4wxDY1tQykkIRVRYxJBYutPhGgjZjEq\nIFcFS9WHFtcbMAHDsrqf+8ecXYfNLjM7s8PsnHk/H4997Dlnvuecz1mG93znO+fMUURgZmaVr6bc\nBZiZWf9woJuZpYQD3cwsJRzoZmYp4UA3M0sJB7qZWUo40M3MUsKBbmaWEg50M7OUOCqfRpKGA8uB\nSUAAVwDPA3cCjcAuYHZE7Dncdo4//vhobGwsvFozsyq0efPmNyOiIVc75XPpv6SVwI8jYrmkQcBQ\nYBHw84i4SdJCYERELDjcdpqamqKlpSW/IzAzMwAkbY6Iplztcg65SDoW+C1gBUBEHIyIvcAlwMqk\n2Urg0sLLNTOzYuUzhv5BoBX4tqSfSVouaRgwMiJeB0h+n9DTypLmSWqR1NLa2tpvhZuZ2aHyCfSj\ngI8A34yIDwP7gYX57iAilkVEU0Q0NTTkHAIyM7MC5RPorwKvRsSmZP5uMgG/W9JJAMnvN0pTopmZ\n5SNnoEfEfwGvSJqQLJoOPAesBeYky+YAa0pSoZmZ5SXf89CvBlZJehaYAiwGbgIukPQicEEyb1ZR\nVq9ezaRJk6itrWXSpEmsXr263CWZFSyv89Aj4mmgp1NmpvdvOWZHzurVq7nxxhtZsWIFH//4x3ns\nsceYO3cuAJdffnmZqzPru7zOQ+8vPg/dBpJJkyZx6623ct5553UtW79+PVdffTVbt24tY2Vmh8r3\nPHQHulWt2tpaDhw4QF1dXdey9vZ2Bg8ezLvvvlvGyswO1W8XFpml1cSJE5k9ezaDBw9GEoMHD2b2\n7NlMnDix3KWZFcSBblVr1KhR3HPPPVxxxRXs3buXK664gnvuuYdRo0aVuzSzgjjQrWpt2LCB5uZm\nNm7cyHHHHcfGjRtpbm5mw4YN5S7NrCAOdKtabW1tDBs2jB07dtDR0cGOHTsYNmwYbW1t5S7NrCAO\ndKtatbW1LF++nMWLF7N//34WL17M8uXLqa2tLXdpZgVxoFvV6u0MryN55pdZf3KgW9Xq6Ojgyiuv\nZNGiRQwbNoxFixZx5ZVX0tHRUe7SzAriQLeqVV9fz4QJEzhw4AARwYEDB5gwYQL19fXlLs2sIHld\n+m+WRldddRULFmRusjV//nyWLl3KggULmD9/fpkrMyuMA92q1q233grAokWLuO6666ivr2f+/Pld\ny80qjYdcrKpt2LCh6zTFtrY2n4NuFc2BblVr8uTJbNmyhVmzZtHa2sqsWbPYsmULkydPLndpZgVx\noFvV6gzzNWvWcPzxx7NmzZquUDerRA50q2ozZsw45AYXM2bMKHdJZgXzh6JW1a699lrWrVvXdYOL\nmTNnlrsks4K5h25Vq76+nrfffpslS5bw1ltvsWTJEt5++22fh24Vyz10q1rt7e1MmjSJtWvX0tDQ\nAGTuYvTcc8+VuTKzwriHblVr4sSJXTe3ALpucuEbXFilcqBb1aqpqaGlpYWLL76Y1tZWLr74Ylpa\nWqip8X8Lq0x+5lrV2rp1K9OnT2fnzp2MHDmSnTt3Mn36dN8g2iqWA92qVkQwZsyYQ25wMWbMGH99\nrlUsB7pVtRUrVhxyg4sVK1aUuySzgjnQrWpJIiLYsWMH7e3t7Nixg4jo+pDUrNLkFeiSdknaIulp\nSS3JsuMkPSjpxeT3iNKWata/IoJp06axdOlShg8fztKlS5k2bZqHXKxi9aWHfl5ETImIpmR+IfBw\nRJwKPJzMm1WM+vp62traGDRoEACDBg2ira3NFxZZxSpmyOUSYGUyvRK4tPhyzI6c8ePH8/jjj3PR\nRRfR2trKRRddxOOPP8748ePLXZpZQfK9UjSAByQF8E8RsQwYGRGvA0TE65JOKFWRZqXwwgsvMHXq\nVO6//34aGhqor69n6tSptLS0lLs0s4LkG+hTI+K1JLQflPR/892BpHnAPIDRo0cXUKJZabS1tbF3\n795DbnCRPW9WafIacomI15LfbwA/BM4Cdks6CSD5/UYv6y6LiKaIaOr8vgyzgUAS27ZtO+QGF9u2\nbfNZLlaxcga6pGGSjumcBi4EtgJrgTlJsznAmlIVaVYKnaconnvuuQwdOpRzzz2361RGs0qkXE9e\nSR8k0yuHzBDN7RHxJUm/AdwFjAZeBi6LiJ8fbltNTU3h8UkbKCQxaNAgDh482LWsc96hbgOJpM1Z\nZxj2KucYekS8BJzRw/L/B0wvrDyzgeHgwYOcfvrp3HfffXzqU59i27Zt5S7JrGC+UtSq3tixYxk6\ndChjx44tdylmRfENLqyqjR8//pAbXIwfP54XXnihzFWZFcY9dKtqra2tXVeG1tfX09raWuaKzArn\nQLeqVV9fz549exg+fDjbt29n+PDh7Nmzx5f+W8XykItVtSFDhrB79+6u284NGTKEjo6OMldlVhj3\n0K1qtbW1cc455xxyT9FzzjnHV4paxXKgW9WSxCOPPML8+fPZu3cv8+fP55FHHvGVolaxHOhWtTqv\nFB03bhx1dXWMGzfOV4paRXOgW1WbO3cuixYtYtiwYSxatIi5c+eWuySzguW89L8/+dJ/G0hqamqQ\ndMiHoDU1NUSEPxi1ASXfS//dQ7eq1tHRweDBg3niiScYPHiwg9wqmk9btKoVEdTV1dHW1sbZZ5+N\nJOrq6mhvby93aWYFcQ/dqtrs2bMPuafo7Nmzy1yRWeEc6FbVVq1axeLFi9m/fz+LFy9m1apV5S7J\nrGAOdKt6N998My+//DI333xzuUsxK4rH0K3qZV/6b1bJ3EO3qjdy5Ei2b9/OyJEjy12KWVEc6Fb1\nbrjhBkaPHs0NN9xQ7lLMiuJAt6rW3Nx8yJWizc3N5S7JrGC+UtSq1uG+hMvf52IDia8UNeuDdevW\nlbsEs6I50M2AmTNnlrsEs6I50K2qjR079rDzZpXEgW5VbefOncyaNYvW1lZmzZrFzp07y12SWcEc\n6Fb1Nm3axJtvvsmmTZvKXYpZUfIOdEm1kn4maV0yP0bSJkkvSrpT0qDSlWlWGoMGDeq6UnT37t1d\nX9RlVon60kO/BtieNf8V4OsRcSqwB/CtXqziHDx48LDzZpUkr0CXdDIwA1iezAuYBtydNFkJXFqK\nAs1KraamhoceeoiaGo9AWmXL9xm8BLgB6Lydy28AeyPinWT+VWBUP9dmdkR0dHRw/vnn+25FVvFy\nBrqkmcAbEbE5e3EPTXu8tE7SPEktklpaW1sLLNOsNLpfLXq4q0fNBrp8euhTgVmSdgF3kBlqWQIM\nl9T59bsnA6/1tHJELIuIpohoamho6IeSzfpPRDBixAieffZZRowY4Uv+raLlDPSI+N8RcXJENAKf\nBR6JiGZgPfDppNkcYE3JqjQrofb29q4fs0pWzKdAC4D/JWkHmTH1Ff1TktmRtW/fPs4880z27dtX\n7lLMitKnQI+IRyNiZjL9UkScFRHjIuKyiGgrTYlmpVVbW8ujjz5KbW1tuUsxK4rP07Kqd+yxx3Lc\nccdx7LHHlrsUs6L4nqJW1WpqatizZw+TJ0/umvfpi1ap3EO3qtY9vB3mVskc6GbAbbfdVu4SzIrm\nQDcDPve5z5W7BLOiOdDNzFLCgW4G3H333bkbmQ1wDnSreo2NjUyZMoXGxsZyl2JWFJ+2aFWtrq6O\nXbt2MW7cuK55fwWAVSr30K2qdQ9vh7lVMge6GdDc3FzuEsyK5kA3A1atWlXuEsyK5kA3M0sJB7oZ\ncP3115e7BLOiOdDNgBNPPLHcJZgVzYFuhnvolg4OdDPgM5/5TLlLMCuaA90MOOWUU8pdglnRHOhm\nwC233FLuEsyK5kA3M0sJB7oZMGLEiHKXYFY0B7oZsGfPnnKXYFY0B7qZWUo40M2A0047rdwlmBXN\ngW4GjB07ttwlmBUtZ6BLGizpSUnPSNom6YvJ8jGSNkl6UdKdkgaVvlyz0rj33nvLXYJZ0fLpobcB\n0yLiDGAK8ElJZwNfAb4eEacCe4C5pSvTzMxyyRnokbEvma1LfgKYBnTeWXclcGlJKjQzs7zkNYYu\nqVbS08AbwIPATmBvRLyTNHkVGFWaEs3MLB95BXpEvBsRU4CTgbOAiT0162ldSfMktUhqaW1tLbxS\nMzM7rD6d5RIRe4FHgbOB4ZKOSh46GXitl3WWRURTRDQ1NDQUU6tZydTU+IQvq3z5nOXSIGl4Mj0E\nOB/YDqwHPp00mwOsKVWRZqU2apRHDK3yHZW7CScBKyXVknkBuCsi1kl6DrhD0t8APwNWlLBOs5J6\n5ZVXyl2CWdFyBnpEPAt8uIflL5EZTzczswHAA4dmeAzd0sHPYjOgo6Oj3CWYFc2BbmaWEg50M7OU\ncKCbmaWEA93MLCUc6GZmKeFANzNLCQe6mVlKONDNzFLCgW5mlhIOdDOzlHCgm5mlhAPdzCwlHOhm\nZinhQDczSwkHuplZSjjQzcxSwoFuZpYSDnQzs5RwoJuZpYQD3cwsJRzoZmYp4UA3M0sJB7qZWUrk\nDHRJp0haL2m7pG2SrkmWHyfpQUkvJr9HlL5cMzPrTT499HeA6yJiInA28EeSTgMWAg9HxKnAw8m8\nWcWJiK4fs0qWM9Aj4vWIeCqZ/iWwHRgFXAKsTJqtBC4tVZFmpSSp68eskvVpDF1SI/BhYBMwMiJe\nh0zoAyf0d3FmpdRbj9w9datUR+XbUNLRwPeBL0TEL/LtzUiaB8wDGD16dCE1mvVZMb3tvqzr8LeB\nJK8euqQ6MmG+KiJ+kCzeLemk5PGTgDd6WjcilkVEU0Q0NTQ09EfNZjllj4vn8/OBBev6vI7D3Aaa\nfM5yEbAC2B4Rf5v10FpgTjI9B1jT/+WZmVm+8hlymQp8Htgi6elk2SLgJuAuSXOBl4HLSlOimZnl\nI2egR8RjQG+DitP7txwzMyuUrxQ1M0sJB7qZWUo40M3MUsKBbmaWEg50M7OUcKCbmaWEA93MLCUc\n6GZmKeFANzNLCQe6mVlKONDNzFLCgW5mlhIOdDOzlHCgm5mlhAPdzCwlHOhmZinhQDczSwkHuplZ\nSjjQzcxSwoFuZpYSDnQzs5RwoJuZpYQD3cwsJRzoZmYp4UA3M0uJo3I1kPQtYCbwRkRMSpYdB9wJ\nNAK7gNkRsad0ZVo1O+OLD/DWr9pLvp/GhT8q6fbfN6SOZ/7iwpLuw6pbzkAHvgP8PfDdrGULgYcj\n4iZJC5P5Bf1fnhm89at2dt00o9xlFK3ULxhmOYdcImIj8PNuiy8BVibTK4FL+7kuMzPro0LH0EdG\nxOsAye8T+q8kMzMrRMk/FJU0T1KLpJbW1tZS787MrGoVGui7JZ0EkPx+o7eGEbEsIpoioqmhoaHA\n3ZmZWS6FBvpaYE4yPQdY0z/lmJlZoXIGuqTVwE+ACZJelTQXuAm4QNKLwAXJvJmZlVHO0xYj4vJe\nHprez7WYmVkRfKWomVlKONDNzFIinytFzcrqmIkL+dDKheUuo2jHTASo/CtebeByoNuA98vtN/nS\nf7M8eMjFzCwlHOhmZinhQDczSwkHuplZSjjQzcxSwoFuZpYSDnQzs5RwoJuZpYQvLLKKkIaLct43\npK7cJVjKOdBtwDsSV4k2LvxRKq5GtermIRczs5RwoJuZpYQD3cwsJRzoZmYp4UA3M0sJB7qZWUo4\n0M3MUsKBbmaWEg50M7OUcKCbmaWEA93MLCWKCnRJn5T0vKQdkhb2V1FmZtZ3BQe6pFrgH4D/DpwG\nXC7ptP4qzMzM+qaYHvpZwI6IeCkiDgJ3AJf0T1lmZtZXxQT6KOCVrPlXk2VmZlYGxXwfunpYFr/W\nSJoHzAMYPXp0Ebszy5/U09Mzxzpf6ft+In7tKW9WNsX00F8FTsmaPxl4rXujiFgWEU0R0dTQ0FDE\n7szyFxFH5MdsICkm0H8KnCppjKRBwGeBtf1TlpmZ9VXBQy4R8Y6kPwbuB2qBb0XEtn6rzMzM+qSo\ne4pGxH3Aff1Ui5mZFcFXipqZpYQD3cwsJRzoZmYp4UA3M0sJB7qZWUroSF4cIakV+M8jtkOz/B0P\nvFnuIsx68YGIyHll5hENdLOBSlJLRDSVuw6zYnjIxcwsJRzoZmYp4UA3y1hW7gLMiuUxdDOzlHAP\n3cwsJRzoA5ykkPS1rPnrJf1lP237O5I+XeQ2Tpa0RtKLknZK+rvk65Q7H18t6VlJ1yrjz5K2L0ha\nL+n04o+kx7rGS7ovuYH5dkl3SRpZxPb+UtL1yfRfSTo/mf6CpKFZ7XZJOr7burNKcRN1SQ2S2iX9\nQYHr70t+N0ra2r/VWTk40Ae+NuB3uodEuUmqVea2QD8A7omIU4HxwNHAl5I2JwIfi4jJEfF14I+A\njwFnRMR44MvAWkmD+7m2wcCPgG9GxLiImAh8E2jo1q6gbxuNiP8TEQ8ls18AhuZovzYibipkXzlc\nBjwBXF6CbVsFcqAPfO+Q+cDu2u4PdO9hZ/W4PiFpQ9IrfUHSTZKaJT0paYuksVmbOV/Sj5N2M5P1\nayV9VdJPk971H2Rtd72k24EtwDTgQER8GyAi3k3qvCLptT4AnCDpaUn/DVgAXB0RbyftHwD+HWju\nrF/S1yQ9JelhSQ3J8rGS/k3S5qTW38w6/m9I+ndJL2X9LX4X+ElE3Nt5kBGxPiK2Svp9Sd+TdG9S\nH5L+NOtYv5j197xR0vOSHgImdP+7S/oT4P3Aeknre/sHTPb59zlq7rEOScMk/UjSM5K2SvpM1qYv\nB64DTpY0Kms7+yR9KVnnic53JsrcjOYnyT7+urd6s7YzJVn/WUk/lDQiWX5Vso1nJH2/8x1Kb8cm\n6SRJG5PnwdbkuWAl4ECvDP8ANEt6Xx/WOQO4BvgQ8HlgfEScBSwHrs5q1wicC8wAlia927nAWxHx\nUeCjwFWSxiTtzwJujIjTgNOBzdk7jYhfAC8D44BZwM6ImAI8AwyLiJ3d6mxJtgMwDHgqIj4CbAD+\nIlm+jMwLwZnA9cA/Zq1/EvBxYCbQ2Que1L2ubs4B5kTENEkXAqcmxzUFOFPSb0k6k8xduD4M/E7y\ndzhERHyDzG0Xz4uI8w6zv+5+rebe6gA+CbwWEWdExCTg35L2pwAnRsSTwF1AdtAPA56IiDOAjcBV\nyfK/I/Ou5aPAf+VR53eBBRExmcwLeOe/xw8i4qPJ9reTeb70emxkXmDvT54HZwBP57FvK0BRN7iw\nIyMifiHpu8CfAL/Kc7WfRsTrAJJ2kvRGyfzHzA6fuyKiA3hR0kvAbwIXApOzeo/vIxM2B4EnI+I/\nkuWihxuDH2Z5T7LbdgB3JtO3AT+QdDSZYZrv6b0bP9dnrX9PUv9zyn+M/MGI+HkyfWHy87Nk/mgy\nx3oM8MPOdxOS+vP2ij3V3FsdPwZukfQVYF1E/Dh5/LNkghzgDmAF8LfJ/EFgXTK9GbggmZ4K/I9k\n+l+AXm+LnXQehkfEhmTRSuB7yfQkSX8DDE/qvD/Hsf0U+JakuuRxB3qJONArxxLgKeDbWcveIXmX\npUzaDcp6rC1ruiNrvoND/927B2+QCdmrIyL7PyqSPgHsz1q0jfcCorPNsWRuHr4TOKFro5kXpf2S\nPhgRL2Wt0tkb70kkx7c36d31JPs4OxN/G5l3Hb3JPgYBX46If+p2HF8g/xelvuqp5h7rSGo5E/gU\n8GVJD0TEX5EZbhkpqTlp9n5Jp0bEi0B7vHc+8rsc/t+7EN8BLo2IZyT9PvCJrMd+7dgiYmPybmMG\n8C+SvhoR3+2HOqwbD7lUiKRHeReHvr3dBZyZTF8C1BWw6csk1Sgzrv5B4HkyPa4/THpUnWeMDOth\n3YeBoZJ+L2lXC3wN+E5nz7abrwLfkDQkaX8+mbfntyeP1wDZ4+CPJUM4/yHpsmQdSTojxzHdDnxM\n0ozOBZI+KelDPbS9n8yY/9FJu1GSTiAzVPHbkoZIOga4uJd9/ZJMb75YPdYh6f3A2xFxG3AL8BFJ\nE8gMX42KiMaIaCTzAfNnc+zj8aw2zYdrGBFvAXuyxrs/z3svvMcAryfPj8NuJzmWDwBvRMQ/k3kn\n8ZFc61hh3EOvLF8D/jhr/p+BNZKeJBOu+3tc6/CeJ/MfdSQwPyIOSFpOZmz9qaTn3wpc2n3FiAhJ\nvw38o6Q/JxPI9wGLetnXrcAIYIukd8mM414SEZ3DSPuB0yVtBt7ivXHhZuCbkv6MzIvWHWTG5HsU\nEb9S5gPeJZKWAO3As2Q+U+je9gFJE4GfJEM6+4DPRcRTku4kM977n2SGPnqyDPhXSa9njaM/K6kj\nmb4r2fdh9VYHmc8ivppsrx34QzK98x9228T3yfxdDvdh5zXA7ZKuSdpnmyDp1az5a4E5ZD5XGQq8\nBPzP5LE/BzaR+btsIfcL2ieAP5XUnhzX7+VobwXylaI2YEjaFxFHl7sOs0rlIRczs5RwD93MLCXc\nQzczSwkHuplZSjjQzcxSwoFuZpYSDnQzs5RwoJuZpcT/By/V39x4Y2qDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfd1cba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"NumberOfOpenCreditLinesAndLoans\"].plot(kind=\"box\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "minUpperBound_NumberOfOpenCreditLinesAndLoans = min([val for (val, out) in zip(df.NumberOfOpenCreditLinesAndLoans, mad_based_outlier(df.NumberOfOpenCreditLinesAndLoans)) if out == True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "newNumberOfOpenCreditLinesAndLoans = []\n",
    "for val in df.NumberOfOpenCreditLinesAndLoans:\n",
    "    if val > minUpperBound_NumberOfOpenCreditLinesAndLoans:\n",
    "        newNumberOfOpenCreditLinesAndLoans.append(minUpperBound_NumberOfOpenCreditLinesAndLoans)\n",
    "    else:\n",
    "        newNumberOfOpenCreditLinesAndLoans.append(val)\n",
    "\n",
    "df.NumberOfOpenCreditLinesAndLoans = newNumberOfOpenCreditLinesAndLoans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    145562.000000\n",
       "mean          8.503689\n",
       "std           4.936663\n",
       "min           0.000000\n",
       "25%           5.000000\n",
       "50%           8.000000\n",
       "75%          11.000000\n",
       "max          24.000000\n",
       "Name: NumberOfOpenCreditLinesAndLoans, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.NumberOfOpenCreditLinesAndLoans.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf1e6860>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4DuBYn0jAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11037668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"NumberOfOpenCreditLinesAndLoans\"].plot(kind=\"box\", grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NumberRealEstateLoansOrLines**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf6e3a20>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJJXE/wek8DH7bhI/KQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110227b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"NumberRealEstateLoansOrLines\"].plot(kind=\"box\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "realNew = []\n",
    "for val in df.NumberRealEstateLoansOrLines:\n",
    "    if val > 17:\n",
    "        realNew.append(17)\n",
    "    else:\n",
    "        realNew.append(val)\n",
    "\n",
    "df.NumberRealEstateLoansOrLines = realNew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1155fe80>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11109940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"NumberRealEstateLoansOrLines\"].plot(kind=\"box\", grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10e74ac8>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11400048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.NumberOfDependents.plot.box(grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "depNew = []\n",
    "for var in df.NumberOfDependents:\n",
    "    if var > 10:\n",
    "        depNew.append(10)\n",
    "    else:\n",
    "        depNew.append(var)\n",
    "\n",
    "df.NumberOfDependents = depNew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b92438>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQJekRhjoktQIA12SGvH/bPLSpAZnJukAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e74c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.NumberOfDependents.plot.box(grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据切分\n",
    "    将数据进行切分，分成训练集和测试集两部分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda\\anaconda\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y = df[\"SeriousDlqin2yrs\"]\n",
    "X = df.iloc[:, 1:]\n",
    "# 测试和训练数据进行3：7的比例进行切分 random_state定一个值是的每次运行的时候不会被随机分\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=123)\n",
    "\n",
    "train = pd.concat([Y_train, X_train], axis=1)\n",
    "test = pd.concat([Y_test, X_test], axis=1)\n",
    "\n",
    "train.to_csv('TrainData.csv',index=False)\n",
    "test.to_csv('TestData.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 探索性数据分析\n",
    "    一般采用直方图、散点图和箱线图等来进行分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAAGoCAYAAABbkkSYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZcj/AwmRfcJkp2tgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bebba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 下面利用直方图和核密度估计画图，Age、MonthlyIncome、NumberOfOpenCreditLinesAndLoans大致呈正太分布，符合统计分析\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)\n",
    "\n",
    "plt.subplot2grid((2, 3), (0, 0))\n",
    "train[\"age\"].plot(kind=\"hist\", bins=30, figsize=(12, 6), grid=True)\n",
    "plt.title(\"Hist of Age\")\n",
    "\n",
    "plt.subplot2grid((2, 3), (0, 1))\n",
    "train[\"age\"].plot(kind=\"kde\", grid=True)\n",
    "plt.title(\"KDE of Age\")\n",
    "\n",
    "plt.subplot2grid((2, 3), (0, 2))\n",
    "train[\"MonthlyIncome\"].plot(kind=\"kde\", grid=True)\n",
    "plt.xlim(-20000, 80000)\n",
    "plt.title(\"KDE of MonthlyIncome\")\n",
    "\n",
    "plt.subplot2grid((2, 3), (1, 0))\n",
    "train[\"NumberOfDependents\"].plot(kind=\"kde\")\n",
    "plt.title(\"KDE of NumberOfDependents\")\n",
    "\n",
    "plt.subplot2grid((2, 3), (1, 1))\n",
    "train[\"NumberOfOpenCreditLinesAndLoans\"].plot(kind=\"kde\")\n",
    "plt.title(\"KDE of NumberOfOpenCreditLinesAndLoans\")\n",
    "\n",
    "plt.subplot2grid((2, 3), (1, 2))\n",
    "train[\"NumberRealEstateLoansOrLines\"].plot(kind=\"kde\")\n",
    "plt.title(\"KDE of NumberRealEstateLoansOrLines\")\n",
    "\n",
    "# 解决中文的显示问题\n",
    "plt.rcParams[\"font.sans-serif\"]=[\"SimHei\"]\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "\n",
    "plt.tight_layout() # 调整子图之间的间距，紧凑显示图像\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 特征选择\n",
    "    变量分箱：\n",
    "    \n",
    "        将连续变量离散化\n",
    "           \n",
    "        将多状态的离散变量合并成少状态\n",
    "              \n",
    "    变量分箱的重要性：\n",
    "    \n",
    "        1、稳定性：避免特征中无意义的波动对评分带来波动\n",
    "        \n",
    "        2、健壮性：避免极端值的影响\n",
    "        \n",
    "     变量分箱的优势：\n",
    "     \n",
    "         1、可以将缺失值作为一个独立的箱带入模型中\n",
    "         \n",
    "         2、将所有的变量变换到相似的尺度上\n",
    "         \n",
    "     变量分箱的劣势：\n",
    "     \n",
    "         1、计算量大\n",
    "         \n",
    "         2、分箱之后需要编码\n",
    "      \n",
    "     变量分箱常用的方法：\n",
    "     \n",
    "         有监督的：\n",
    "            \n",
    "            1、Best-KS； 2、ChiMerge（卡方分箱法）\n",
    "        \n",
    "         无监督的：\n",
    "            \n",
    "            1、等距； 2、等频； 3、聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy.stats.stats as stats\n",
    "\n",
    "# 自定义自动分箱函数\n",
    "def mono_bin(Y, X, n=20):\n",
    "    r = 0\n",
    "    good = Y.sum()\n",
    "    bad = Y.count() - good\n",
    "    while np.abs(r) < 1:\n",
    "        d1 = pd.DataFrame({\"X\": X, \"Y\": Y, \"Bucket\": pd.qcut(X.rank(method=\"first\"), n)}) # X.rank(method=\"first\")\n",
    "        d2 = d1.groupby(\"Bucket\", as_index = True)\n",
    "        r, p = stats.spearmanr(d2.mean().X, d2.mean().Y) # 使用斯皮尔曼等级相关系数来评估两个变量之间的相关性\n",
    "        n = n - 1\n",
    "    d3 = pd.DataFrame(d2.X.min(), columns = ['min'])\n",
    "    d3['min']=d2.min().X\n",
    "    d3['max'] = d2.max().X\n",
    "    d3['sum'] = d2.sum().Y\n",
    "    d3['total'] = d2.count().Y\n",
    "    d3['rate'] = d2.mean().Y\n",
    "    d3['woe'] = np.log((d3['rate'] / (1-d3['rate'])) / (good/bad))\n",
    "    d3['goodattribute'] = d3['sum'] / good\n",
    "    d3['badattribute'] = (d3['total'] - d3['sum']) / bad\n",
    "    iv = ((d3['goodattribute'] - d3['badattribute']) * d3['woe']).sum()\n",
    "    d4 = (d3.sort_values(by = 'min'))\n",
    "    print(d4)\n",
    "    cut=[]\n",
    "    cut.append(float('-inf'))\n",
    "    for i in range(1, n+1):\n",
    "        qua = X.quantile(i/(n+1))\n",
    "        cut.append(round(qua,4))\n",
    "    cut.append(float('inf'))\n",
    "    woe = list(d4['woe'].round(3))\n",
    "    return d4, iv, cut, woe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 自定义分箱函数\n",
    "def self_bin(Y, X, cat):\n",
    "    good = Y.sum()\n",
    "    bad = Y.count() - good\n",
    "    d1 = pd.DataFrame({'X': X, 'Y': Y,'Bucket': pd.cut(X, cat)})\n",
    "    d2 = d1.groupby('Bucket', as_index = True)\n",
    "    d3 = pd.DataFrame(d2.X.min(), columns=['min'])\n",
    "    d3['min'] = d2.min().X\n",
    "    d3['max'] = d2.max().X\n",
    "    d3['sum'] = d2.sum().Y\n",
    "    d3['total'] = d2.count().Y\n",
    "    d3['rate'] = d2.mean().Y\n",
    "    d3['woe'] = np.log((d3['rate'] / (1 - d3['rate'])) / (good / bad))\n",
    "    d3['goodattribute'] = d3['sum'] / good\n",
    "    d3['badattribute'] = (d3['total'] - d3['sum']) / bad\n",
    "    iv = ((d3['goodattribute'] - d3['badattribute']) * d3['woe']).sum()\n",
    "    d4 = (d3.sort_values(by = 'min'))\n",
    "    print(d4)\n",
    "    woe = list(d4['woe'].round(3))\n",
    "    return d4, iv, woe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          min       max   sum  total      rate       woe  \\\n",
      "Bucket                                                                     \n",
      "(0.999, 25474.0]     0.000000  0.031258   464  25474  0.018215 -1.370448   \n",
      "(25474.0, 50947.0]   0.031260  0.158295   542  25473  0.021277 -1.211902   \n",
      "(50947.0, 76420.0]   0.158306  0.560505  1323  25473  0.051937 -0.287684   \n",
      "(76420.0, 101893.0]  0.560523  2.000000  4607  25473  0.180858  1.106155   \n",
      "\n",
      "                     goodattribute  badattribute  \n",
      "Bucket                                            \n",
      "(0.999, 25474.0]          0.066897      0.263382  \n",
      "(25474.0, 50947.0]        0.078143      0.262550  \n",
      "(50947.0, 76420.0]        0.190744      0.254326  \n",
      "(76420.0, 101893.0]       0.664216      0.219742  \n"
     ]
    }
   ],
   "source": [
    "dfx1, ivx1, cutx1, woex1 = mono_bin(train.SeriousDlqin2yrs, train.RevolvingUtilizationOfUnsecuredLines, n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        min  max   sum  total      rate       woe  \\\n",
      "Bucket                                                              \n",
      "(0.999, 11322.333]       21   33  1317  11322  0.116322  0.588970   \n",
      "(11322.333, 22643.667]   33   40  1078  11321  0.095221  0.365212   \n",
      "(22643.667, 33965.0]     40   45   984  11322  0.086910  0.264743   \n",
      "(33965.0, 45286.333]     45   49   934  11321  0.082502  0.207865   \n",
      "(45286.333, 56607.667]   49   54   839  11321  0.074110  0.091495   \n",
      "(56607.667, 67929.0]     54   59   667  11322  0.058912 -0.154296   \n",
      "(67929.0, 79250.333]     59   63   511  11321  0.045137 -0.435158   \n",
      "(79250.333, 90571.667]   63   71   331  11321  0.029238 -0.885924   \n",
      "(90571.667, 101893.0]    71  107   275  11322  0.024289 -1.076444   \n",
      "\n",
      "                        goodattribute  badattribute  \n",
      "Bucket                                               \n",
      "(0.999, 11322.333]           0.189879      0.105363  \n",
      "(11322.333, 22643.667]       0.155421      0.107870  \n",
      "(22643.667, 33965.0]         0.141869      0.108870  \n",
      "(33965.0, 45286.333]         0.134660      0.109386  \n",
      "(45286.333, 56607.667]       0.120963      0.110387  \n",
      "(56607.667, 67929.0]         0.096165      0.112209  \n",
      "(67929.0, 79250.333]         0.073674      0.113841  \n",
      "(79250.333, 90571.667]       0.047722      0.115737  \n",
      "(90571.667, 101893.0]        0.039648      0.116337  \n"
     ]
    }
   ],
   "source": [
    "dfx2, ivx2, cutx2, woex2 = mono_bin(train.SeriousDlqin2yrs, train.age, n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pinf = float('inf') # 正无穷大\n",
    "ninf = float('-inf') # 负无穷大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             min   max   sum  total      rate       woe  goodattribute  \\\n",
      "Bucket                                                                   \n",
      "(-inf, 0.0]  0.0   0.0  3571  85522  0.041755 -0.516577       0.514850   \n",
      "(0.0, 1.0]   1.0   1.0  1694  11086  0.152805  0.903933       0.244233   \n",
      "(1.0, 3.0]   2.0   3.0  1268   4383  0.289300  1.717911       0.182814   \n",
      "(3.0, 5.0]   4.0   5.0   324    744  0.435484  2.357188       0.046713   \n",
      "(5.0, inf]   6.0  13.0    79    158  0.500000  2.616699       0.011390   \n",
      "\n",
      "             badattribute  \n",
      "Bucket                     \n",
      "(-inf, 0.0]      0.863033  \n",
      "(0.0, 1.0]       0.098908  \n",
      "(1.0, 3.0]       0.032804  \n",
      "(3.0, 5.0]       0.004423  \n",
      "(5.0, inf]       0.000832  \n"
     ]
    }
   ],
   "source": [
    "cutx3 = [ninf, 0, 1, 3, 5, pinf]\n",
    "dfx3, ivx3, woex3 = self_bin(train.SeriousDlqin2yrs, train[\"NumberOfTime30-59DaysPastDueNotWorse\"], cutx3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    145562.000000\n",
       "mean          0.571777\n",
       "std           0.546250\n",
       "min           0.000000\n",
       "25%           0.173933\n",
       "50%           0.359088\n",
       "75%           0.770648\n",
       "max           1.566521\n",
       "Name: DebtRatio, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.DebtRatio.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          min       max   sum  total      rate       woe  \\\n",
      "Bucket                                                                     \n",
      "(0.999, 33965.0]     0.000000  0.235771  2075  33965  0.061092 -0.115632   \n",
      "(33965.0, 67929.0]   0.235790  0.544337  2154  33964  0.063420 -0.075755   \n",
      "(67929.0, 101893.0]  0.544357  1.566521  2707  33964  0.079702  0.170297   \n",
      "\n",
      "                     goodattribute  badattribute  \n",
      "Bucket                                            \n",
      "(0.999, 33965.0]          0.299164      0.335836  \n",
      "(33965.0, 67929.0]        0.310554      0.334994  \n",
      "(67929.0, 101893.0]       0.390283      0.329170  \n"
     ]
    }
   ],
   "source": [
    "dfx4, ivx4, cutx4, woex4 = mono_bin(train.SeriousDlqin2yrs, train.DebtRatio, n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        min      max   sum  total      rate       woe  \\\n",
      "Bucket                                                                  \n",
      "(0.999, 33965.0]        0.0   3400.0  2747  33965  0.080877  0.186213   \n",
      "(33965.0, 67929.0]   3400.0   6845.0  2553  33964  0.075168  0.106810   \n",
      "(67929.0, 101893.0]  6845.0  17694.0  1636  33964  0.048169 -0.366981   \n",
      "\n",
      "                     goodattribute  badattribute  \n",
      "Bucket                                            \n",
      "(0.999, 33965.0]          0.396050      0.328759  \n",
      "(33965.0, 67929.0]        0.368080      0.330792  \n",
      "(67929.0, 101893.0]       0.235871      0.340449  \n"
     ]
    }
   ],
   "source": [
    "dfx5, ivx5, cutx5, woex5 = mono_bin(train.SeriousDlqin2yrs, train.MonthlyIncome, n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             min  max   sum  total      rate       woe  goodattribute  \\\n",
      "Bucket                                                                  \n",
      "(-inf, 1.0]    0    1   714   3827  0.186569  1.144240       0.102941   \n",
      "(1.0, 2.0]     2    2   410   4343  0.094405  0.355698       0.059112   \n",
      "(2.0, 3.0]     3    3   464   5970  0.077722  0.142990       0.066897   \n",
      "(3.0, 5.0]     4    5  1111  16589  0.066972 -0.017460       0.160179   \n",
      "(5.0, inf]     6   24  4237  71164  0.059539 -0.143048       0.610871   \n",
      "\n",
      "             badattribute  \n",
      "Bucket                     \n",
      "(-inf, 1.0]      0.032783  \n",
      "(1.0, 2.0]       0.041419  \n",
      "(2.0, 3.0]       0.057984  \n",
      "(3.0, 5.0]       0.163000  \n",
      "(5.0, inf]       0.704814  \n"
     ]
    }
   ],
   "source": [
    "cutx6 = [ninf, 1, 2, 3, 5, pinf]\n",
    "dfx6, ivx6, woex6 = self_bin(train.SeriousDlqin2yrs, train.NumberOfOpenCreditLinesAndLoans, cutx6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             min   max   sum  total      rate       woe  goodattribute  \\\n",
      "Bucket                                                                   \n",
      "(-inf, 0.0]  0.0   0.0  4656  96308  0.048345 -0.363143       0.671280   \n",
      "(0.0, 1.0]   1.0   1.0  1206   3608  0.334257  1.927706       0.173875   \n",
      "(1.0, 3.0]   2.0   3.0   785   1542  0.509079  2.653019       0.113178   \n",
      "(3.0, inf]   4.0  15.0   289    435  0.664368  3.299519       0.041667   \n",
      "\n",
      "             badattribute  \n",
      "Bucket                     \n",
      "(-inf, 0.0]      0.965195  \n",
      "(0.0, 1.0]       0.025296  \n",
      "(1.0, 3.0]       0.007972  \n",
      "(3.0, inf]       0.001538  \n"
     ]
    }
   ],
   "source": [
    "cutx7 = [ninf, 0, 1, 3, pinf]\n",
    "dfx7, ivx7, woex7 = self_bin(train.SeriousDlqin2yrs, train[\"NumberOfTimes90DaysLate\"], cutx7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             min  max   sum  total      rate       woe  goodattribute  \\\n",
      "Bucket                                                                  \n",
      "(-inf, 0.0]    0    0  3226  37444  0.086155  0.255190       0.465110   \n",
      "(0.0, 1.0]     1    1  1863  35769  0.052084 -0.284705       0.268599   \n",
      "(1.0, 2.0]     2    2  1254  21766  0.057613 -0.177973       0.180796   \n",
      "(2.0, 3.0]     3    3   293   4356  0.067264 -0.012805       0.042243   \n",
      "(3.0, inf]     4   17   300   2558  0.117279  0.598247       0.043253   \n",
      "\n",
      "             badattribute  \n",
      "Bucket                     \n",
      "(-inf, 0.0]      0.360353  \n",
      "(0.0, 1.0]       0.357067  \n",
      "(1.0, 2.0]       0.216014  \n",
      "(2.0, 3.0]       0.042788  \n",
      "(3.0, inf]       0.023779  \n"
     ]
    }
   ],
   "source": [
    "cutx8 = [ninf, 0, 1, 2, 3, pinf]\n",
    "dfx8, ivx8, woex8 = self_bin(train.SeriousDlqin2yrs, train[\"NumberRealEstateLoansOrLines\"], cutx8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             min   max   sum  total      rate       woe  goodattribute  \\\n",
      "Bucket                                                                   \n",
      "(-inf, 0.0]  0.0   0.0  5107  96788  0.052765 -0.271004       0.736303   \n",
      "(0.0, 1.0]   1.0   1.0  1243   4000  0.310750  1.820084       0.179210   \n",
      "(1.0, 3.0]   2.0   3.0   517    994  0.520121  2.697225       0.074539   \n",
      "(3.0, inf]   4.0  11.0    69    111  0.621622  3.113136       0.009948   \n",
      "\n",
      "             badattribute  \n",
      "Bucket                     \n",
      "(-inf, 0.0]      0.965500  \n",
      "(0.0, 1.0]       0.029034  \n",
      "(1.0, 3.0]       0.005023  \n",
      "(3.0, inf]       0.000442  \n"
     ]
    }
   ],
   "source": [
    "cutx9 = [ninf, 0, 1, 3, pinf]\n",
    "dfx9, ivx9, woex9 = self_bin(train.SeriousDlqin2yrs, train[\"NumberOfTime60-89DaysPastDueNotWorse\"], cutx9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             min   max   sum  total      rate       woe  goodattribute  \\\n",
      "Bucket                                                                   \n",
      "(-inf, 0.0]  0.0   0.0  3579  60340  0.059314 -0.147067       0.516003   \n",
      "(0.0, 1.0]   1.0   1.0  1344  18421  0.072960  0.074617       0.193772   \n",
      "(1.0, 2.0]   2.0   2.0  1125  13668  0.082309  0.205319       0.162197   \n",
      "(2.0, inf]   3.0  10.0   888   9464  0.093829  0.348948       0.128028   \n",
      "\n",
      "             badattribute  \n",
      "Bucket                     \n",
      "(-inf, 0.0]      0.597755  \n",
      "(0.0, 1.0]       0.179839  \n",
      "(1.0, 2.0]       0.132091  \n",
      "(2.0, inf]       0.090315  \n"
     ]
    }
   ],
   "source": [
    "cutx10 = [ninf, 0, 1, 2, pinf]\n",
    "dfx10, ivx10, woex10 = self_bin(train.SeriousDlqin2yrs, train[\"NumberOfDependents\"], cutx10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 相关性分析和IV筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwMAAAJDCAYAAABXDcj7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3Xd4HNXZ8OHfM9t31SzJRe42GJtm\nU0wxJRhCx5SQBEILNYQEEngpL+RNSEwLTkINJQE+YnoLhFAMAUwxxQYDLrhh44K7XNTb1jnfH7OW\nVtLuyiq2RPTc17WXpNmzZ549M7Nz6kqMMSillFJKKaV6H6u7A1BKKaWUUkp1D20MKKWUUkop1Utp\nY0AppZRSSqleShsDSimllFJK9VLaGFBKKaWUUqqX0saAUkoppZRSvZQ2BpRSSimllOqltDGglFJK\nKaVUNxOR/iLyUZbnPSLymoh8IiIXZdrWXtoYUEoppZRSqhuJSB/gcSCUJdmvgC+NMYcCPxKR3Azb\n2sXdkYCVUjuYSI//1+BX29O7O4Sspk2Z0N0htOm462d3dwhZPfrR7t0dQpu8a/zdHUJW4aDd3SG0\n6eofftbdIbRpyn/Gd3cIWV127KLuDqFNL83ZtbtDaFPp+EGyU3e4E++1Aj8HLk3Z9LAx5uGUvxPA\nmcArWbKZCNyQ/P1DYHyGbe+3JzZtDCillFJKKbUDJSv+D2d5vhpAJGt7KASsT/5eDvTPsK1ddJqQ\nUkoppZRSPV8tEEj+noNTj0+3rV20MaCUUkoppXofl7XzHl3jS+Cw5O/jgG8zbGsXnSaklFJKKaVU\nDyIiRwF7GGPuT9n8OPCGiBwO7AF8hjNFqOW2dtHGgFJKKaWU6n1cO3e98vYwxkxM/nwPeK/Fc6tF\n5BickYDfG2MSQLpt7aKNAaWUUkoppb4DjDEbgBfa2tYe2hhQSimllFK9T9fN5f9O01JQSimllFKq\nl9KRAaWUUkop1fu4e96age6gIwNKKaWUUkr1UjoyoJRSSimleh9dMwDoyIBSSimllFK9lo4MKKWU\nUkqp3qcH/p+B7qAjA0oppZRSSvVS2hhQSimllFKql9JpQkoppZRSqvfRBcSAjgwopZRSSinVa+nI\ngFJKKaWU6n10ZADQkQGllFJKKaV6LR0ZUKoXeJqzmc7RTOWinb7v+X8fxKc3jSBncIRJLywgf0Q4\nbbpEVHjzvD1Z+WoxoQFRjn5kCcOOriARFab9ZC82fppPoDjGMY8soeSg6i6Ps/IzN2XvenDn2ww8\nK4qn0LT5mtJ/eQkMS5C/f6LZ9rL33BhbKD461iWxLfj7QGbfPJycwRGOf35R1jJ8+7zdWfVaMcEB\nUY56eClDj67g9dP2YtPsvMZ04XI3p709n0Hfq+pUXLFXAsQeC2H1tfHeVIlVYqdNZxIQvSuXxMd+\nXPtE8f62CvEmn7Mhcn0BnrPrcO3rlJeJQfS2fBKf+JBCG+911bjGRzscZ+Q9L+GXfVh9bIK/qsfV\nN/OxNTY0TA0Qm+PGPSZB8LJ6xAPxb1zU/imEBJzXeifECJztHIfIW15iczxIoU3gJ2Gs/LbPnZbi\nb3mJ/zOAFNp4rqnD6p++LMEpz/jDQRKfe7D2iOO5sg7xJPOZ5sOe7YFiG895DUhB81giv8/BfUYY\n117xdsc45+9D+GjyruQODnP6P+dRMKIhY9qFT5fw1dRB+PvEmfjHZRSOqm98btolezLksErGXrC+\n3TG0ZKb5MU8HoTiB/F8NMiB7uZn7c2CmF8bGkOtqEC+YMJg/58JiDxwWwbqizkl7XmHzDCoF640y\nzAdezF9zwO9slh/Xw7HbF+/Cv5fwefJaPu75xeRluZannzeGb5PX8sSHlzLk6EreOG3PZtdypNzN\nKW9/xcBOXsv1LwWpezgPq3+CgtvLcQ1KpE1nElBzewHhGX68+0fJv7m88VrepvznxeRcWo13/yhl\n5/bF3uJqfM6utCh+ZROuAenz3+n0q0UBHRlQgIhcIyJrRGSliEzqRD7Xisi1XRlbMt/JIlIrIpUi\n8r6I7JXm+ck7Ik4R+amIbBGRehG5s52h9wivcxKX8wCGnf+ht3VBiM9uHc45X87myL8u5f1fjc6Y\n9ss7h+LyGC7d+DETblrJe78cA8CCRwZRvFcdP9/wMYdPWc47l+ze5XFGSoWy990MuyJMv5NjbH7N\n2+Zrtr7joWauq9X2ys9dlM/wdFlsWxeE+Py2YZz5xRd8795vmPGrURnTzr1rCJbHcPGGmRw0eRUf\nXO6knfTvhVy8YSYXb5jJOQtnkzciTP8DazoVl73STfzJEP5HyvFcWUP03ryMaeOvBjClLgIvbsHa\nLUb8pSAAJg7Rv+Rhz2te3vEXguA2BP61Bc9FtUTvzu1wnIm1FuFXfOTeXEvgvDANTwSypo++58Xe\nKuTdW4NreILI295kPi78p0bIv6+G/PtqGhsC0ZkeYgvcBC5qwCqwaXja3+4Y7dUW8ZcC+P5cjefi\neuKPBrO/p7d9mC0WvoeqsEYmSEzzOds/9GLP9eC+rB7pY4hNbZ5P/F0vZnHHzs3NC3L4+JZduGjO\nTI69bwlvXZH5Olz7SQGz7x7OsfctYcj3ynntp2Mbn5vx+11Z+OSgDsXQklnlwjwbQO6rRH5Rh3kw\nJ/sL3vDDJgt5qhzZNQ6vOOeCeSoIOQZ5phwMmA+9iAusZ8obH3JNDUxwGqRmpRv5dW3Tcz9IX6Fv\nqWxBiC9vG8YZX3zJ4fcu58Nf7Zox7by7BmN5DBdsmMkBk7/lw+S1fOK/F3HhhllcuGEWZy38nLwR\nYfod2LnOkdhyN3WP5lH41GZyr62i+i8FGdM2vBQisdFF3zdK8YyOUv9c8zJveDVIbI6v8e+ip7bQ\n961S+r5VSp+HtuIeHes5DQHVSBsDvZyI7AecB4wGTgceFZEO3S2MMXcYY+7oyvhS3A/0Bd4D3hAR\nXxvpM9reOEVkKDAZOAYYBZwpIod2dL/dZSoXciO3dMu+l/+7L3ucV0rOwCgDJ1TTsNVDrC79x07f\ncbUc9cDX+AvijJy0lZp1ziEuXxyiZILT6zX4iApq13f40GdUu9hF3r4J3HmGwFCbRB3YWTqiI5uE\nRAPkjG1+U7OjUL/MRcGE9ve6ZrLylWLGJMuwZEI14bLMZVg8tpaJDyzDVxBnxKQyate1Lqt59w5m\n71+sx+3P3IO6PRIf+3AdG8YqtnHtGYMqC5OhozjxkQ/PmU4Pu/vUBhKfOHHZX3qxBsWx9mg+gmLt\nEsd7VQ2Sa3BNiGC2tG50ba/Ylx68h0ax+hjcoxKYGsFEsqT/woPvhCjiBt/3I8TmOB+HidUWrqFp\nKjEGAuc34Opv4z0khl3a/ljt2V5cR0SQQoM1OoGpFkyW+qX9mQfXKWHEA67jwiQ+3zbMAu6f1WOV\n2LgOj2A2Np0npkpIvOzH2qdjo1XL/t2fvX+6gdyBEQZPqKRhq5doXfr3Gqlyc8JDiyjevY5xF66n\nfJnTKNmyOES43MPuZ27sUAytzPLC9yNIkY3sHoc2ys184kV+2OCMokwKYz5NltsnPuSMBsQCmRTG\nzGrdGWCeCyJnJ0c3VrhhZPsrtKteKWK38zYRGhhlQJvXch3fe+AbfAUJhme4lr+6dzB7/mIDbn/7\nR6JSRT4I4D+xHldfG+/YKHalhWlI33kUed9P8NxaxAOBH9cRmdHU+LXLLeqm5uI9OP1BqJuaS+ji\nznVCdDmXtfMePVjPjk7tDHsCW4wxDcaYecCNNA5+9izGmJgx5hYgDhy5E3bpBs4yxswzxqwH5gCD\nd8J+u9SL/Igiyrpl37Xr/BSPrW38OzQwQvXq9D2zI04sw5fv3GBXTy+k5GCnt6tor1oWP1FCtMbF\n/L8NZugx5V0eZ7xK8KVML3DlGWIVmUdSvP0M/U+JIS2SWF4YeI5Tkewqtet8FO9d1/h3qCRCzer0\nl+jwE8sby3Dt9D4MOLh5j2EiKix9pj97XFTa6bjMFgtrl6ZGjxQlMJvSVw7NFldjWskxmErn1mMd\nGMVzbn2r9K6Do0iOU8FJfOlt1VhoD7tCcA1pOrZWH4O9NfOtzy6Xxkq/BMFUOwc5sdpFw1N+qi7P\npfbPQewqZ7v30BiuvgZTD+FpPjwHtD9WU25hDWuqXEqhjdmSOcbU9BICkjG6johi9bcxdRD/tx/X\nwU2xxKYGcP0gDAUdawRWr/PTb2xTRS5nYISqDOfhridupWT/amINFh/fsgujf7QJgOLd6zju/iVI\nF9U8zFYLGZHS8C60IcM5CMBWC5LpJWSgKhlItcC2aTH9bNjQPA+z3AUug+ySTLPCjbk1F/vMQuzb\nc7M2QFI513LK52FJNOO1PCzlWl43vQ/9D25eiU5EhWXP9GOPizrfsLI3u3CPajpXXH0TJDamL8dE\nSlorx2BXNqWruSuf0IU1WEWtG0qJMovYIg++721nYamdStcMqA+BB0Xkb8AtxpiHAUTkQpyGQQD4\nvTHmkeT2D3B66c8F8owxR23LaNtUHWPM5JRt5wK3AAJMNsY8JiIXABONMRek5DnZGPOBiNwE/CyZ\n/hZjzINpYp4PjAH+k+lNicgtyXwWA4XAVcaYD9LFKSITcUYAvgTOT77mOGPMSmBlMk0QOBi4UkSO\nA24wxhyZfO5uYIMx5i8i8hjwGXAAcLgxZlQyzanAXUAu8DpwiTGmc12z26k7Z0SaBHjzmm7WnlCC\nSGX2j514RPh08kiOuGsZALudsYnZtw/n5ZP2oWa1n0kvfdWpmNY/6SW8tvmNLjQ6geVr6l2zvGCH\nBUjf49ayEbAjmYTgaVaGdptlmIgIn900nMPvXNFs+4qX+jL0mHK8OZ0fpjcJQYIpp7DfYGotIE3e\nCSA1bTJJW+VoohCbmoP38u3rTay9O0hiZfNj6xkbb5znD4DPYOqz7NimeXo7WdEelsA3KYJVZGh4\n2k/4FR/BnzZVbGr/HMLebBH4UW3LHJuJTglhL29+/Fz7xqBFjNRlj7FZ+hZFHr0lF7PJwnO2U26J\neW6otnB/P0p0Scdu+yYBvlbXcvZB5Nd+Opblr/flp598BnTuurFvyoWlLfY3PgrBlHLwt1Vu0jz9\ntnLzpcTmN9Cit9684UdOcI61CQOHRJDz6yFoMFNy4fUAnNL2e3Cu5aaD5d6Oz8NERJh90zAOa3Et\nr3ypmCHHVODJ6fxtxNhghZryEb/BrsnQYrMFK5RShslTIvKpD7vCInBKPdE5rUdWwq8GCZxcv1M/\nO7eLrhkAtDHQ6xljVovIIcCdwDIRuRz4ArgaGA94gDki8qoxZlPyZX8ErgVmZMtbRMYAU4AJOB+7\nn4rIl1nSFwK/AfrjjFo9DKRrDNQCGSeHJt/P2TgNhpHA59niTJoAvAiUJNMfD7yc8vx1wHvGmFUi\nshZ4UkSKjDFlwCSaLx/7DXATThltcwtwJfBm8j2NAJp9uovIpcClAA9t++U7ztcn3uxmF29wIVb2\nIe1Zvx9J331qGH68MwIw45rdOPxPyxlz1iYatnp44Yj9OeeL2bgDHbsJDjqv9fyfLW96kpV/h4nR\nva2oFL4+MaLNytBqs2f10z+MoHhcLcOObz6K8vVT/dn/+jVdEpfk2snKf1JUMpaZ5DoNBdl2zKLb\nV7ixqTlYu8ZxHbR9i4dz/qf1KEPDs/7mlf8scYLTY2zqBEk2Dk2ywzR4YVPF33dUlLr7ms/Hz51c\nR+R9D3UPB8n9vzoy8d7Q+rnYE4HmFdCoZB+3DyUrvf5tMTZ/Q74pNcTf8RK9L4j3t7XEnwjguT5z\nTNvD3ydOuNV5mP1aPv2f81j1ThH/+tE+/GLFh52qCFp/aN0gtP9fELbzHAQgx3bSb5sit+20sgzG\nJBsEEQFpel8mBsz2ws+d8hM/yK9TyvK4MOa17OtQtvH1iTe7lhPbUYaz/zCc4nF1DD2+otn2pU/1\nZ7/r127Xftti5drNKv8mIpAhLsm1sWsEV7IxaqLO1Kzae/MpuCPzCHTDm0H6/LV7RqhV23SakMIY\ns8AYcyxOBfpvwNE4lejFOL3wOThrCrb5hzHmVWNMW19fcAzwujFmrTFmA07l+rg06bZ9fFcBS4G7\nk689P0O+IZwGQSYHAW8aYyqNMXOS76Etm4AHjDGxZPr8xuBExgGX4TSQMMbEgTeAk0VkD6DSGLMq\nJa83jDGPGmNSa2If4zQOfoYz4tG8m8fJ92FjzHhjzPj/hoYAQP/x1Wyc5RSlMbB5Ti45gzJP2F71\nZhHLXuzPUQ8ubdxW+mkeRXs4N99AcQxPKEHVt107k80/yKZhjdUYZ3iDhTuvc/Nwu0q//WvY+GlT\nGW6Zm0MoSxl++2Yhy1/sy5EPftNse6TKxdavcig5tHPfOrKNNTqGvcjTGJe9zIP0TT/ikJrW3mg5\nPd9tSHzmJfGBD+//dG5xpGtEgvhyV2OciW9dWH0yNyRT0ye2COI12LVC9POmSpxdYTV+asXmuzHJ\nSqXnwBj2+vbfVq1d4tjLmmI0q1xIYeYYrV0S2MuceOxNVmPDJTHH3bgewnVIDLPOhVnpwmy1iP42\nl/Al+dgzvcTuCJH4pH1Lw0rGV7F+VkFjjKVz8sjNcB6u+agPdZud3uERx5TRUO4hVt/xdR+ZyKg4\n5mt3Y0wsd0Nxlk6CUXFIjoyYUgu2TcMfkYBvksf3mxZ5zPPA6DjbVqmZTRZmTkrZlVnbXZPqu38N\npZ/mNcbrXMuZG7qr3yxkxYt9OeLBZc22R6pclH2Vw4AuupY9u8eILfA2xhVb6sHVN305evaINqZN\nrHchfkPsay+JUhflF/Vly3EDCE8PUvm/RYTfdhpJ8VVuxGt65sJht7XzHj2Yjgz0ciJyK/CNMeZx\nY8yrIvI+TkX4CWPML5JpcoHUT/1P27EL0+L3dLWAQQDGmISIHIDTGDkJuEVE9kyTfm+cRku2fab2\nD23PVbjKGLMttsYYRSQPeB74ZbJBs82LwCXAAOCfLfJqVT7GmF8mRyy+D3wpIkcZYxZtR1zfacNP\nKGPGNaMYclQFZQtz8BfFyB2SvgJRuTzAW+fvwWmvz8df0DQdIW9EmPl/G8R+V62ldHYelSsC5A3t\n2nmnwd0SbH7DQ3CkTXST4AoaPAU9ozEw/IRyPr52VwYfWUH5ohD+onjWMnzngjGc/NoCfAXNFzGv\nfddZQ2B1UZ3MdVCU2IO5xPeLYla5kTwbq1/6CoTr+2Gid+YhBTbxF4O4Dsuyghew17mI/DEf35QK\nJLdzx8EzLkbDs36iu8ex17uQHBurKHOe3oNj1E8NYOU1EHnLh2f/OOI1NDwdwFVSh+QZItO8ePZz\nhgxisz3El7oInBEhNtuDqwMLS619Y8QeD2LtFcesdTnfbFOcOUbXYVFiDwWRPEN8mg8ruU7BnuXF\nXuLGc06YxEwP1q4JrN0T+J9oqjRG7w/imhht91eL7nLCFqZfPYZhR5WzZWEOgaIYeUPSX4er3y9k\n7t+HcPITX7H6vSJyB4XxhnZARXB8DB4JYcbF4FsX5NlIhkosgEyMYO7LgXwb8+8ATIg0bX8oBBfW\nYR4PIhObzk/zhRcZ13wdiLk/B6Y4ZWpeCyAnZ/6K1VTDTihn5rW7MOjIyjav5arlft69YDQnvbYQ\nX0Hzslv3bh/6d+G17D0kTM09+YTHR4iv8GDl2xkr7v5jG6i+vQCrwKb+uRx8RzTg3SdKv/eb1i5U\nTS4gcHI93v2dhk5kph/v+OzXvOpePbuponaGNcCFIhIQkX44Fe2XgRNEZECyITAf2KMDeb8DTBKR\nQSJSAvwAeBuoBoYAiMiJwC7J33cD3k0+rsepaBdty0xE3CJyA05F/4Ms+50JnCgiBSIyPvme2pLp\nzvss8B9jzMsttr+DMwJxBk7DICsRWQSsB24DlgBjs7/iv4MvL8Gkfy5gzt1DWfdhASc8tYgNM/P5\n96RxrdLOe2AwkSo3r5w6lodKDuOhksOo3ejliDu/Ycv8XJ7e/0Bm/WEkxz66GE+oa5dbuPww8Owo\nFZ+4qV/louQM5ybWsNpi/eNd/+1F7eHNS3DCC4uYd88Q1n9YwLFPLmbjzDxendT6tF7w4ECiVW6m\nnbY3jw48hEcHHkLdRqcXb8OHBa0WFHeGhAzem6qIvxAkMd+D93dVJBZ6CN/Q+msJXeNieM6sI3pf\nLgQNnguyT1mJ/zsAdULk/wqo/0Ex9T8oxpR17HYlAQhdUU/kPz7iX7sJ/sKpuMW/cVF7Z+uv8HSP\nSeA7MUL9UwHwG/ynhREvBM9roPYvIWp+m4NVYuM/1anc+H8SJvGti6rLc4l95iV4wfZVDJvFGATv\nNbXEX/djL3LjudIpH/trF9E/tp4Rae0Rx3VKmNhUJ0b3Gc4+3ec1YFa6CV+Uj/2JF8+lnZsalMqX\nl+D0F+cx+67hrJlRyKlPf8W6mQU8f9J+rdJO+N9V2AnhrwOP5IPfjuK057ZncLb9JGSQ39Zg/hXA\nLPAg/+sMGJvFbuzft/6qW9k7jvywAfNQDgQNco5TbvL9CHJgFPNADuwWh5NSGjkLPDAmZaF8fxv5\nUQPmigLMNfnO140et30VXW9eguNeWMz8ewaz4cN8jn5yCRtn5vH6pL1apV3w4CCiVW7eOG0vpg6c\nwNSBE1Ku5Xz6d+G1bOUY8qeUU/9MDrE5XvJvriA630vFVUWt0nr3ixI6t5aaO/ORoCH0s7bX88Tm\nePHs3fH/E7JDuWTnPXowaeoMVb2RiLhx5rBPwun9v9UY86iIXAz8Dmf06B5jzJ3J9B+QXOybJq/J\nsF0LiIM4jYI4sBAYB9yYXEA8Bbgg+fKpxpjfJPO9Fojh9LpfkTrNJsN+J+NM7VmA06C4uq0FxMaY\nicm/H8NpbMwF5gFbaVpqNsUYc08y3dPAaGPM+JT9PgZ8YIx5rEXZnImzfiKEs8D4LGNM5qlOIj3+\nwrzant7dIWQ1bcqE7g6hTcddP7u7Q8jq0Y+6/n86dDXvmh755WeNwsGd8j0BnXL1Dz/r7hDaNOU/\n49tO1I0uO7bnD/S+NCfz/zXoKUrHD9q5teY9svwHwq62eEuPbRHoNKFeLjn/vdUUdWPMo8CjabZP\nzJLX5DTbngKearGtHjgsQx43ADekybdV3m3st/E1yQZMxvTJRsLElL8vSHk67cWbbEQtBpp9tU2L\n16Zufx5nupFSSimleoIe/v3/O4s2BpTqmNmAl5RGhFJKKaXUd402BtR/vWyjGZ3Is/VEWaWUUkqp\n7xhtDCillFJKqd5HpwkB+m1CSimllFJK9Vo6MqCUUkoppXqfHv6VnzuLjgwopZRSSinVS+nIgFJK\nKaWU6n10zQCgIwNKKaWUUkr1WjoyoJRSSimleh9dMwDoyIBSSimllFK9lo4MKKWUUkqp3kfXDAA6\nMqCUUkoppVSvpSMDSimllFKq99E1A4CODCillFJKKdVr6ciAUkoppZTqfXTNAKAjA0oppZRSSvVa\nOjKglFJKKaV6H10zAOjIgFJKKaWUUr2Wjgwo1QNdbU/v7hDadJd1dHeHkFXVOLu7Q2hT7bXe7g4h\nq0Ss5/cXeSM9O8bdPwt0dwhtKj01t7tDaFPxOl93h5BVaTjU3SG0qSA/2t0hqB5KGwNKKaWUUqr3\n0QXEgE4TUkoppZRSqtfSkQGllFJKKdX76MgAoCMDSimllFJK9Vo6MqCUUkoppXof/WpRQEcGlFJK\nKaWU6rV0ZEAppZRSSvU+umYA0JEBpZRSSimlei0dGVBKKaWUUr2PrhkAdGRAKaWUUkqpXktHBpRS\nSimlVO+jawYAHRlQSimllFKq19KRAaWUUkop1fvomgFARwaUUkoppZTqtXRkQCmllFJK9T66ZgDQ\nkQGllFJKKaV6LW0MKKWUUkop1UvpNCGllFJKKdX76AJiQEcGlFJKKaWU6rV0ZECp77D5fx/EpzeN\nIGdwhEkvLCB/RDhtukRUePO8PVn5ajGhAVGOfmQJw46uIBEVpv1kLzZ+mk+gOMYxjyyh5KDqnfwu\n4GnOZjpHM5WLdvq+t/l6K8zdBEEPHDUMcn2Z0yZsmLEG1lZDwA2HDoFBuTB9FWypb0oXScDxI2FA\nTidje7g/824ZQnBQlCOfXUruiEj6uKLCh+ePYu3rhQQGRDn07ysY+P2qZmk+vGhXSiZWMeqnWzoU\nS+I1P/EnQ0hxAs+N1UiJnTadSUD83hzsmT6ssTHcN1QjXjANEJ+Sh73Yg3V4BM+va5183/MRvzcH\nkuXu+kk97tMbGvOLPxPEbLLw/E9tu2Nu+MBD3WterD6G/J834OprMqY1NtQ85SMy1413dIK8i8OI\nB8pvCWJXNvUi2jVC0e11uIoMxoaqewMET4ziHZ1od3wtlS60WDfbhTfHsNvxcfx5bb9mxXsucksM\n/XZ3jsfKD12UL2/q74tHYZeJCfqOSX+8tsfXDw1gbvI8POq5r7OehzPO3421ryXPw4eWM+j7VdSX\nevjnruPxFsQB6H94NUc9u7TD8bS0I4+z+A1VDwWILXPh6meT97MwniEdL0uAlf+vL0tuG0hgUJSD\nnlpBaHg0bTo7Knx+8Qg2TivA3z/Gfg98S7+jajq175Yqn81l630FuAckGHjvZrxD4mnTmQRs+n0x\ntdODBA4KU3LHZiwvJCot1l/Rj+hKD54hcUqmbME7Ip5xe4+hC4iB7RgZEJHJIlIrIptFZL2IXNPV\nQYjIYyJyQTvS3ysiZ3Zif5NFZErK389tz/4z7TeZ3+Q0268VkWs7Gud2xNPp/EXkZyKyQURKReTy\nFs8dJCJfJ4/9dSIyUUQ+TXn+MhF5rDP7707bjpuIDBeR0izpzhSRe3dmbNtj64IQn906nHO+nM2R\nf13K+78anTHtl3cOxeUxXLrxYybctJL3fjkGgAWPDKJ4rzp+vuFjDp+ynHcu2X1nhd/odU7ich7A\n0H3DteUNMG8TnLobTBgEs9ZnT79wC1gCP9kT9h0As9Y5248eAWft6TxOHw05XigOdjK2BUHm/3Ew\np3w2n4PvWcmnV43MmHbR3QOxPIYz137Ovr9fy8xfNU+74f08VjzVr8Ox2KtcxJ8O4n2wAvcVtcTv\nz9zKsV/3Yza58D5bhoyKkXg5AEDiyRDkGLzPl4ENiRlO7d+sdOO+shbfC2X4Xihr1hAwmy0Sr/hx\nX1DX7pjj6yzqpnnpc2M9uWfi3Ar1AAAgAElEQVSFqXnWnzV9wwwPia0WxX+pwz3Upv5dLwCFN9ZT\nfGcdxXfWUXBtA+6httMQSEDNE36iy1ztji2dujJh3Rcuxp4ZY8T3Eqz6sO0+uzWfudiytPntfOT3\nEoy/KMb4i2Lsd34Mfx7kd6LyWr4gyLw/DubU2fOZcM9KZl2Z+TxcePdALLfhJ+tms+8f1jDril2c\nPOaH2PW8zZy17nPOWvd5lzYEdvRxrv2XD88Qm7731uI/ME7tP7P0FmyHqoUBvp5SwlEzFzPuzjXM\nu3poxrTf3Nsfy204ceV8dv/dBuZeNaxT+24pstRD2QMFDH95A/1vLGPzzUUZ01Y+m0tsvZtdPlqD\nf48IFY/nA1D2UD75p9Wy68y15J1Sy+bbi7JuVz3L9jaJ7jfG9AMOBa4XkXE7MKY2GWOuNMY8vyPy\nFpECEbmqK/ZrjLnDGHNHF8Q0eUfkLyKjgT8BE4FDgFtEJLU2eANwW/LY/6Oj+/muM8Y8b4y5srvj\naGn5v/uyx3ml5AyMMnBCNQ1bPcTq0l/SfcfVctQDX+MviDNy0lZq1jk3svLFIUomOD3Hg4+ooHZ9\n525wHTGVC7mRW3b6flOtqYZdC51RgX4hCCcglqWDtzAAhwwGnwuG5kFdrHWaRVth9yJwd7Ljac2r\nhexyzhaCA2P0O7iW8FZ3xuPcZ2wdE+5bia8gwZCTKqhf5218Lh4WPrtqJMNOK+twLPYnPlzHRJBi\nG2uPOKbawjSkT5v42If7R/WIB1ynhLFn+hq3u35Sj1jgOqUB+xMnRnu5G9klfY9h/MEcXD9oQPpk\n7unNJDLPjX9CDFeBwbOLjV0rmPQd2k76OW6Cx0YRNwQmRonOa10Zr3/DS2iS04sbXeLC1dfGM7Lz\nIwIAFSuFvqNtvCHIHWCIN0AizfnVGEs5xCNQNCpzRX/rUouCoU6eHbXm1UJ2PXcLwYFR+k2oIZzl\n86ZwbD2H3L8CX0GCoSdVUJc8D8vmhSgc1/4G3fbY0cfZOyZB6IcRxAfevePYFZ3rvNjwegFDzy4j\nUBKj6KA6omVu4hnKM3/vBva5Zw3eggQlJ1TSsN6bNl1H1UwPkXdaLe7+CQL7RkhUWNj16d9f7dsh\nCi+uQrxQcE41te86vR3R5V78+zgFHjwwTHyTK+v2HsMlO+/Rg7XrNmWM+Rb4FMjcBfndVwCkbQx0\noz/soHwnAW8ZY5YZY1YC/wFOSnm+D7AWwBjT8RqE2iFq1/kpHts0ZSI0MEL16kDatCNOLMOX71RW\nVk8vpORgZypQ0V61LH6ihGiNi/l/G8zQY8p3fOAtvMiPKKJ7T6+6KPRJ6UgMuqE2SwVsSB54k/e0\n9bXQt0Xvf8KGFRWwW2EXxLbeS+HeTXOPgiVR6lanb7QNOaESb/I4b3g3n74HNZ0f828bwrAflNFn\nr/q0r90eZquFpAzxS5GN2Zzh5r7VQpIVZAkZqEreDKsEGZzc3t/GbHBeb1a4id+UR+SHRcRuzcUk\nZ7zZcz3Yn/gwiz3E/pyLWd++1lWiQnAPaqooW/k2ibLMedgVFu7BTnor6EwTafZ8tRD71sI7zikH\n754JQieln97REZFaIVTU1OjxhCCSZUZIoI8zCiBZ6hobv7IYMLZzjZW6dT767N1UkQ8OjFKb8Tys\naDwP16ech2Xzclhw1yCeGXgArx06lqpl2Xvv22NHH2f/AXEkmV10iRvPyM5NEWpY7yV/r6aWtL8k\nRv2a9JX8AcdX4UmW5+b38yg8sP1T5bKJl7rwjWk6h939EsQ2pB+Rim1y4RvtpHXlGhLlzvXr2y1K\n1Yu52LVC5fO5BA9tyLpd9Szt+lQVkaHAeGCpiFwoIitFZKOI/Cz5/OUickdK+ndFZELy9+tEZI2I\nLBWRE9rYT8Z8kn83m1YkIheIyNPJR5mIvCTifDSKyNXJGGeIyKsicmuW/T4DfA4MSU6b+U+L59s7\nnanZ9CERyUvmW5qcemNEJD/53GXJ8lkvItcnt/1p2/SV5GsWZcs/ue1cEVklIt9uizVL+YwEVqe8\nfA0wXEROTe73EOBfyX1nbAAmp9l8myzrUhH5SkQGJJ87VURWJN/vP0Scj1MROVBE5ia3P5xyvI4R\nkSUisklE7k7NP937Tv5+s4j8OfnefMnto0TkIxHZkny/weT2k5LlPB/Yq+2j2Fh+j23n+830vtKW\nQ2eYBHjzmipmnlCCSGX2KQXxiPDp5JEc8L/fArDbGZvY8Ek+L5+0D/PuG8L461Znff2OsLP7S6av\ngmcXNX/YpqlyD+CxILoddaeEDXNLYWyLmTffVjlrCDxd0AlmEoInrykYd8gmUpX9OCciwtybh7D3\ntc58p4qFQda92Ydx/7euc8EkgFBK77zfQG36I2hsgWBThckkpPE1jRXX5OtNA1iHRvDcXYn3uTKI\nC4nXnIZt/LkgskcM68QwkmcTva4Ak2HKceX9frZeE2r2ICFIoClm8YHJ1h6ywUpJb1rU+Ro+9uA/\nJN74HrJVwtvy9TQ3X/zD0+xhbHB5m/bv8hgSkcw7aWv/NaWCN8R2rTvIxiQEb8p56AkmiLbxeeOc\nh0MZe51z3uWOCHPUc19z9obPGf7Drcy+bkSHYumO49z4shqhYbqH4LGdawCaBLhTr+ugTawq+wdG\nIiIsuW0gu/1PxlmtHZMQrFBTAUjQYFdnuEUlBCsnpbCS12L+j2uo/lcO637en7r3gvQ5uybr9h7D\nZe28Rw+2vdFdISKbgW+Av+Ac/qtxGgb7AJNFpD/wEnAcgIjkAMOBT0XkaOB8YBzwA2BqMn0mafNp\nI8YfAs8Bw3CmM+0jIrnALcAYYCbwlTHmd5kyMMacDRwArDXGDDDGHN/GPtvFGFOdzHcA8DDw/4wx\nVSLiB84DDgZGAdeKSI4x5vpkWpKv2zNb/iIyBpgCfA+nIn+ziOydfLpV+QB+IHUgNQoEjDGvJPc7\nEzg9ue+2JncOBAYkf64BzkpuvwW4EigBYsAIEfECTwMXAEOBEcBpIlIMPAH8OLltoogc28Z+AS4G\nqoAxxjQODD8F/DG532rg5yISAKbinINH4hzrjmr1fjO9r2T6VuXQMkMRuVREvhCRL756+PU2A/D1\niTer/McbXIiVfRrFrN+PpO8+NQw/3hkBmHHNbhz+p+Wc+eGXnPPlbN46f0/iDT37Q6uzUuf1b3v4\n3M0r/3GzfY2UOaXOlKHBLSpaKypgZEHXxOvrEyda2VRJSDRYbR7nOZOHUjiujsHHVWIMzPrVSCY8\nsKJZJbMjJLdF5T8iGQtKcmxInfawre4kYLaFERGwQALguaoWyTeIB6wTwpi5HgDM127cv67BdXAU\n92V14HJGEdIpuCLcON9728MKGUzqlIcYWQ+uBE3zKRItRojCn7rxH5hl2KgdxpwUb5zXv+3h9juN\n9m3seOYy3h5blloUZ5lCtL28feLNKv/xcPvOQ4AD/ria4v2c0YXdLytlw/v5HYqlO49zzbM+/IfE\nmo1CdIS3IEEs9boOW7TVRbTkloHk713PgGO79kserPwEdk3Tzk1YMtYOXXk2iZSGgh11yrD0t8UM\nemgTQ58uZfDUUtZf3i/rdtWzbPeaAZwKeS0wDTgKp1d5MTAfyAFGG2NKgQpxRhC+D7xujDHACcBT\nxpgKY8xi4DPg8Ew7y5JPNl8YY14zxtQCS4F8nEZLHPAA3na83x1KRI4HTgR+BWCMCQM/xWkQvAAU\nAsUdyPoYnLJaa4zZALxMslFF+vKpx2kQbONLbusIA0w2xtjAl8n8AT4GrgV+BtxijFmBM81sOPAW\n8C2wP7AHMAGYZ4xZaIypN8bsa4x5O82+Wn7ELzDG3GaM2QKQbAQegFPxXwecjNMgHA1sMcZ8aYwp\nxymfjkr3fjO9r0zl0DxDYx42xow3xowfe+mkNgPoP76ajbPyk6+FzXNyyRmUeZLsqjeLWPZif456\nsKldV/ppHkV7ODfnQHEMTyhB1bddN3T/XVEcgM3JGRDGQFmDs34gm7XVsKrKWTuQKppwFiT368T8\n7FRF+9Wy+bPcptjmhQgOzNwrue4/BXz7ryIm3L8SgLq1Xsq/CvLej8bw3JDxLLx7IJ9dPYIFdw1s\ndyyyWxx7sacxFnu5GylOXyly0jqVR7PRavykkZEJzLLk9mVuKLYxmyzsL1MKvMxq+rQOGaRfSq+l\nxzQfnWiDe3iC2EpXY8yx1S6sLGsPPCnpE1sESZm5Ed9oOWsgijrXqMomp5+hZpM0xlu3RfC24/2m\nMgYqvrUoGN75xkDx/rVs/jTlPJybQ3BQ5vNw7X8KWPVSMYfc73zUJaLCN0/2bXy+br0XcaU0DDtp\nZxznhhkeEpstQqd0flpYn/3qKJ+d0xhv5bwg/izXdelbeax7uZB97l3T6X235N8rSsNcf2MskcVe\n3P3TD7/59o4QnudMD4uudWP5nTIKz/Pj3TW5vmJYnHipGzssGbf3GDoyALTjq0WNMfUi8g/gl8BK\n4AljzC+gsfK1rRbyEk7lf1+cXtLGLFr83tZHQKZ8MkmtXG3L2wa+SD5WA2ckt4eB1L48L7BTJrKJ\nyBDgAeC4ZCMAEdkF+BCYDFzDdk5fySBTOacrn5U4oxHbDANmZ8k7jFNW26SWW6kxjYOyjTEYY34p\nIofgNOq+FJGjcCrzy40xuwMkR0ZcOI3MRuJMDYsDLb8DcRBOJX+blqNGAoS3jaoke+z9wC40L5/O\n3CHTvd9M7yttORhjFrXMtD2Gn1DGjGtGMeSoCsoW5uAvipE7JH1joHJ5gLfO34PTXp+Pv6DpQz5v\nRJj5fxvEfletpXR2HpUrAuQNTf/1pP/NBufB7A1QkgMVYWdhcE6WNXrVEfhoDRwz0kmbakON0xCw\nuuh+N/j4Sj7/3+GUTKyiclEQX2GcnCHpKw3Vy/18eNEojnllCb4CZ6gjZ2iUc8uaLuu5Nw8hZ3i4\nQ18tah0QJf73EIl9vJhv3Uiu3ayinsp1ZIT4PTlIviHxcgDXIZHk9jDxv+XgvqSW+NQQriOd8y32\n11y8f3F6kBOvBHCd6ny0WHvHSLzjx316A/YXHkxYkJLtn//u3StO7Qs+PGMSJNZbWDkGV2Hm24/v\nwDg1T/qwcgz10z149226XqILXHjGdM1C4UwKhtl8+4mH/EE29eWC22/w5XYsr7qtgidg8HRB+37w\n8RXMvm44JUdWUrEo1OZ5+NGFu3HMK4sbz0OX1/DVnwaTMzRC4bg65t02hKGTyjs1zSrVjj7O0eUu\nal/xUvibeqQLvpS9/7FVfHXDEPoeUU314gDewjjBwelHnGpX+Pji0hEc8tJyvAVdf/6Fjqhny+2F\nBA9uIPKNF6vAxpPhGsubVMemG4twFdpUPJZHztHObdAzOEbF4/nknVhHzbtBrBwby28yblc9S3ub\nKvfj9F7PAE4QkQHJhsB8mnpAXwKOx5mG8kly25vAOeJ8U88Y4CCcntJs0uWTTbqz6yCgDhhhjDnC\nGLMpuX0ecIyI5Cbnek9IvgeAMqBIRILJR/oVmR0gIh7geeB6Y8zylKf2xelJ/gdO73KLvkbKRGSY\niHgkucYgg3eASSIySERKcKbDbOtZT1c+rwPHishoERkJHIsz8pPJUpxpPmOSFexJwNws+SPOOof1\nwG3AEmAs8DUQFJHDk3PnnwQuAWbhTO/aIzn3/084ZVENFItIQEQGAqdniRFjTDWwUETOSW66HbgJ\nWAaUiMg+IlJA0xSejkj3fjO9r0zl0Cm+vAST/rmAOXcPZd2HBZzw1CI2zMzn35Naf9nXvAcGE6ly\n88qpY3mo5DAeKjmM2o1ejrjzG7bMz+Xp/Q9k1h9Gcuyji/GEOt+L+F3jdcFRw52vDC2tg4nJb/nb\nVAdvr2ydfslWiNrN1x/UJ+/jpXWtFxR3Kra8BEc+t5RF9w6k9KM8jnh8GZtm5fLOqa2/BnbJ3wcQ\nq3Lx7unOKMBzQ8ZTv7GNIY52kJDB8/tqEi8Fsb/y4P5NDfYiN7Hftp6Qbo2N4fpxA/EHcyBgcJ3n\nVBpcx0SwDooQvzcXa7cY1slhpL+N+8f1RC/rQ/SqAqxDIljHO40E9y9qMfM8RH5cROy+XDw31CDt\nWIthBSD/sgYa3vEQXeYi7xKnkRFbblH519Yf797dEgSPjVH7nA/LD6GTmxrY0WWuLvvWoEzcXhh9\nfJyN811Ur7cYdYxTSa3ZKCx5vX210Or1Qs6Arql4efMSHPXcUhbeM4jSD/OY+MQyNs3M5e1T0pyH\nfyshWuVi+um78+zgA3h28AHUb/Rw2MPL+eSyXXlx9P6YhDDh3jQXVwft6ONc/5bTEK2YEnTWKVzX\nuaE/T57NQU+vYPl9/dn6US4H/GMlZZ+G+OT0XVulXflQP2JVLmb9eFemjRjHtBHjaOjC69qVYxh4\n32YqpubTMNvPwDu20DDHx7qftZ7NHTwgTJ+Lq9h8ayFWyKb4igoA+t+2lZr/hPj21IFUv5TDgD9v\nybq9x7Bk5z16MGlr9k1yoabfGHND8u+XcSr3CeB3OKML9xhj7kx5zUc48/MvT9l2Hc60mDBwpTHm\nzZTnHgM+MMY81mLfrfJJl16chbITjTEXJP/+AKeX/QucCqwbZ4rT58ClQA1wD87cdBu41xjzl5T8\nfwdcjtNYmpD8pp1scU4GMMZMzrZdRI4A3qN5T/e5yTin4TQE3saZ4nL5tikyInIRcDNO7/YPjDEf\nZdqviJyLMz9dcKaxPJapfIwxH4jIJcn0VnLb31LyakyXsu2nyVh8wLs48/VLkuUyvGVc4vxfhilA\nCGd62FnGmFoRORj4O9AfmA5cbIyJJqdQ3QPkAs8YY65L5nkfzjqINcBGYHMy/0xlPxp4FGc0YA5w\nrjGmQkROB+7FmQ71TbLsH8MZJUmdIvWmMebHybxalt/wLO830/tKWw5kcLV5t8d3ndxlHd3dIWR1\n8bie36gxn8/q7hCyembGqO4OoU35PXxa2y7zdv7X9bbX7vd83t0htOn1xzrdf7JDHXZ21/3PhB1l\n0fqe/x3/X48asXNrzb+esPPutX+d1WNbBG02Br7LROTXQJ4x5tZkr/xLwKPGmFe6OTSlstLGQOdp\nY6DztDHQedoY6BraGOg8bQyk8T+H7rx77d2f9NjGQM9e0dB5M4AfiMgGnJ7fOpzebKWUUkoppXq9\nLlgG03MZY+bjfKOLUkoppZRSTXr4fwbeWf7bRwaUUkoppZRSGWhjQCmllFJKqV7qv3qakFJKKaWU\nUmn18H8GtrNoKSillFJKKdVL6ciAUkoppZTqfXQBMaAjA0oppZRSSvVaOjKglFJKKaV6H0v7xEFH\nBpRSSimllOq1dGRAKaWUUkr1PrpmANCRAaWUUkoppXotHRlQSimllFK9j/6fAUBHBpRSSimllOq1\ndGRAKaWUUkr1PrpmANCRAaWUUkoppXotHRlQSimllFK9j/6fAUBHBpRSSimllOq1dGRAKaWUUkr1\nPj1ozYCIPArsAUwzxtya5vlfAGcm/ywAPgMuB1YmHwC/MsYsaO++tTGgVA80bcqE7g6hTVXj7O4O\nIatH5/f8gc/+j1d3dwhZ9TuwsrtDaFOiONLdIWQVOL3nl+Gbj4zr7hDaZI/v2dfKx8+M7u4Q2nTg\ntGB3h9C2l7s7gO4hIqcDLmPMBBH5h4iMMsZ8k5rGGPM34G/J9PcBjwNjgWeNMdd3Zv89/26plFJK\nKaVUV3NZO++R3UTgheTvbwOHZUooIoOA/saYL4CDgUkiMltEHhWRDnXya2NAKaWUUkqpHUhELhWR\nL1Iel6Y8HQLWJ38vB/pnyepykiMEwOfA0caYAwEPcGJHYtNpQkoppZRSSu1AxpiHgYczPF0LBJK/\n55Chs15ELOBI4LfJTV8ZY7bNlfwCGNWR2HRkQCmllFJK9T6W7LxHdl/SNDVoHPBthnSHA58ZY0zy\n7ydFZJyIuIDTgPkdKoaOvEgppZRSSinVJf4NnCcidwFnAItEpNU3CgHHAR+m/H0z8CQwD5hljJne\nkZ3rNCGllFJKKdX7tL2wd6cwxlSLyETgGODPxphS0vTyG2P+r8XfC3G+UahTtDGglFJKKaVUNzLG\nVND0jUI7lTYGlFJKKaVU79P2XP5eoWeMjyillFJKKaV2Oh0ZUEoppZRSvU8PWTPQ3bQUlFJKKaWU\n6qV0ZEAppZRSSvU+umYA0JEBpZRSSimlei0dGVBKKaWUUr2PrhkAdGRAKaWUUkqpXktHBpRSSiml\nVO+jawYAHRlQSimllFKq19KRAaWUUkop1fvomgFARwaUUkoppZTqtXRkQKn/ApWfuSl714M732bg\nWVE8habN15T+y0tgWIL8/RPNtpe958bYQvHRsS6N8eutMHcTBD1w1DDI9WVOm7BhxhpYWw0BNxw6\nBAblwvRVsKW+KV0kAcePhAE5XRpqWk9zNtM5mqlctON3lqLhAw91r3mx+hjyf96Aq2/mY2tsqHnK\nR2SuG+/oBHkXhxEPlN8SxK5smhtr1whFt9fhKjIYG6ruDRA8MYp3dCJj3unUvhCi6u/5uPonKP7L\nVtyD07/eJKDi1j40vB/ANz5C0R/LEG/T83VvBKl7JUS/h7Y0e1355D749o0QOrWejqj7Z4iah/Jw\n9UvQ5y9luAdljq/qtgLC7wfwjo/Q57ZyxAvR+V7KLitGQk6ZB06oJ/+aKrac1Y/EFlfj6+0Ki36v\nl+IuaV/5tbR+ah9W/bkfvpI4e01dQ2BY9mtwyZUDKTionpKzK5ttb1jtYc7JIzjo4+W48+xOxbRN\n3Yduat/w4iowFFwSxl2c/TysetZHZL4b76gEBRc456FdBxUPB4hvElxFhoKfhnH3d/Kpe89DeL4L\nV6Eh9wdRXHltf4Zt0/BSkPpHcnH1S5B7ewWuLMe5dko+0Rl+PPtFyb25otl5CFB5WRHBn9Xg3T+K\nXS3U/KaQ2FwvriFxcm+pwL1bfLvjahVnF1zL24Q/c9PwiYc+VzcAkCgXah73E19v4Rpok3dBGNd2\n3AeyWb1KWPa14A/A/gfYBEOZ09o2zP1C2FQq+Hwwdl+bvv2anp8/VygshCHDOheT2jF0ZECp77hI\nqVD2vpthV4Tpd3KMza9523zN1nc81Mx1tdpe+bmL8hmeNK/onPIGmLcJTt0NJgyCWeuzp1+4xVnX\n9ZM9Yd8BMGuds/3oEXDWns7j9NGQ44XiYJeH28rrnMTlPIBh5y42i6+zqJvmpc+N9eSeFabmWX/W\n9A0zPCS2WhT/pQ73UJv6d51zofDGeorvrKP4zjoKrm3APdR2GgIJqHnCT3RZ63OhLdFvPFQ/nMeA\n50rpc0MFFVP6ZExb+88c4hvcDHxnA97do9Q8ndv4nF0nVN2bT8G1zSu0VQ/kUfd6ltpHG2LfuKl5\nJI++z2wi//pKqqYUZExb/2KI+AY3/d/eiGdMjNpnnNZlbJmHnEtqGDB9IwOmbyT/mioA+j67uXFb\n0SNb8IyJdbohULvYx6o7+nHAeyvY7U8bWHb9wKzpV97ej9IX0r+nZb8pYdhVW7usIRBbb1H7ppfi\nGxrIOyNC9fNZWvJA/UceEmVCvz/W4RmSoO595zOl9i0vgYNi9J9ST+DAGNUvOfk0zHYTWewi/5wI\nVr6h+sW2P8O2iS930/CPXAqe3ELo2ipq78jPmDb8ryD2RjeF0zbhHhOj4bnmvQjhVwPE5zS9t/oH\n8nCPjlH0bim+Yxuo+2vmvNuMs4uuZQA7DLX/8pF7ZqRxW+3zPnz7xim+ow7f2DjVj2fPvy3VVbBs\nqXD4RJu9xtos/Cp7dXHlckEsOOZ4m9G7GxbMb0q/dImwfm0PXahryc579GDaGFDqO652sYu8fRO4\n8wyBoTaJOrCjmdNHNgmJBsgZ27zyYkehfpmLggkd7/nKZE017FrojAr0C0E4AbEsdafCABwyGHwu\nGJoHdWk6SBdthd2LwL0TPsWmciE3csuO31ELkXlu/BNiuAoMnl1s7FrBRLKkn+MmeGwUcUNgYpTo\nvNaDv/VveAlNck6Q6BIXrr42npHtr8g2vBcgeHI9rn42vnFR7AoLuz79Da/h3QC5P61BPJBzRi0N\nHwQan6t6MB//4WG8o5oOcmyFG7vKInhcx0YEAMLvBwhOqsPVz8Y7Lopd6cJuyBDfewFykvGFzqgl\nnIwvttSDZ3T23vnaR3PJuaS6w3Fus+WNPErOrMRXEif/gAZiZS4SdenjrfvaR6zCRf/Tqlrn82Yu\n4dVeBp1f3umYtgnPdxE4KI6rwOAdaWPXCXaW8zA8z0XO0THEDcEjYoS/cs7D+EYLzwjnXPOOskkk\nR6uMgbyfRHD3MwQOiBPftP0XdXSGH9+J9c55PDaGqbQwGY5z9P0AgXNqEQ/4f1RH9MOmCrNdblH/\nWC6eg8ON2zzjIwQvr0YCBu+hEezNHf+w6cprue4VH76947gHOY09E4foIjf+7znnqv/gGPEV7W/g\npyrdKAweYvAHoE8hRKMQz3JryM0z7D3O4PFCvwGGsDNgQU2189qSQToi0JNpY0Cp77h4leAb0NQD\n6MozxCoy90J4+xn6nxJDWiSxvDDwHOfm09XqotAnpaMq6IbaLHWsIXngTd7L1tdC3xa9/wkbVlTA\nboVdH2s6L/IjiijbOTtLkaiQxhs+gJVvkyj7/+zdd5gdZdn48e89p7ct2U1bEhJKCiXU0EECCNLL\nK4LCC4IgYEFQQHgVNYgKioj09lNKKJEqRVDpvZcQmgkkIb1sb6fOPL8/ZrLn7O45u5vNZrO49+e6\nzpVkzjPPuc+cZ2aeOil92XYaLPzj3PRW1J0O1On9ZiG7yCK4vXtXD25jEzush5ZjT7Gt8hGYnP8R\nfSNt7BXFKyD2Kh/Bye7nWAmDU+9+h+wiP62z4+SW+Km7cASZj9weZP/mOSp/1rhedyh7tQ//pC7x\nLS8dX8BLayUMToMX36dBmq4oZ+V+Y6n7XnW3Y2/XWWQ/ChKekeqW57pKLw8Q2yafT3BMjtSS4j3k\n0SlppvxhRbfj42Rh/vCXci4AACAASURBVMVjsUKGud/elFUPl613XABOo0WgUzk02PWlrzF2g9VR\nbq0IOK1uWn+NQ/LVAE4K2l/2E5rqNgyiu+XwVxucJLT+O0Bkx753SDirffi3zKe3qkuXQ6egTFjx\n/O8M0HZVGdFTWrCq8t8zdGAK8bLKvBnCv23/p04O1LmcWykknwuQW2PRdEuY7CI3D2OD8drOuWU+\nJL5+le9UCsoKBkJCYUj20DYfPQYC3qBy7Rqh0rs2xxMwbfvBHlNdBz5r8F5DmK4ZUKqfROQS4HTA\nBn5ujJklIj8BLgDmAU3AB8aYi0XkVOAXQAT4pTHm1v585rJZQVJLOt/oYlNsrFD+wm8FwUkJUPxm\n0LURMNC6zusHGJfIV+4BAhZk+tAZbTvw3krYrcuMiUVN7hqCwPp1fvXZYNzIGq8Lk1vY+QsFp9lI\nJP87Sih/wy/KAasgvekySyT5coDwnrmOMrBeZcEBK5b/AIkYnJYSNzyHjnn3AMZ2P7jlzgT+8Tni\nx7WSW+Rn9ZmjGPvICnxVAzC9xRasggqRRBxMyfikc+XJiy8wNUP81BZ8Y22aryin5dYyKi7KT2dq\n/3uMyJHtA3JOGRv8ifxJ4Ys65JqLx1vq81Y/Uk52jY+JP1mNFTDMu7AGf5lD1QGtfY6j/qZ8BXOt\n0DY2Ei44lkGDaS99jcHQKT3e14rulaX2iijZL3zYDcKIc5OdP/vaCLk1Fokj+z4iZGyQLuXQtBY/\nQKZLOVwbV+b1EE6Dj/CRSbLvdZ8C5TRaJO+NUX5d3zoENuS53P7vIL6RDtF9s+RWWTT+KUrVb9oI\n7Zij6foIwW1tki8ECO/a9wbVW69bNDZ03jZqtMHvz3++32fI9qEtZNsw7xNh62luwBv6fqMGhjYG\nlOoHEdkU2AeYDJQD74rI34FLgRrgIsD2GgLbAD8BpgMBL+2jxphVXfI8AzgDYNTR11Kxa/eFqpuc\n1L0Xd82TAa/y7zJZBqf2WsJXN+u+7c3lnSv/OdO3EN9d6U4ZGtelg/PzBthuVPF9vqwqfti9d7n1\n/pBX6fL08ttK1OC0C761jcMuN+/U634qzkl237EfrDKnU+XfpKVkT76UuQ0FK+oWApNxv0RmbpCK\ncxqJ7Od+99SrYdLvhIgetP4xSpnTaWTEpErHZyUcTIuAV/kyaXe/iovzFf/osW00XFDVab/kE1FG\nXFe73rECBCpsck0Fi5JTss7ncfM7EWpOaqDmRDfu5KIgtf9MrFNjYMRZ3cth80PBzlNvsqWPJbiV\nWJMU8Mqh8eqlTXeFGfG9JMHNHXKrhYabw4z8Wf63rv5pkvaX/TTdGabqx30rA1aZwWntUg5LHDcr\n4ZYJ39pKdkYwKWi7poyyK0pPq2q9opzw4e34t+hbBXtDnsu5hT7iX88Q2iFHCMh85CMzz0fZqSmS\nLwfIfu7DaREiB/R9xG+X3bs3vj/+UMhm8w0+2xZEeh9tmPepUFZuGDW6zx+/cQ3xufyDZWiPWyg1\nRBljFgPnAucBdwOjgZz3CgBB8ufX/sDmwMfAHCAOTCmS5y3GmOnGmOnFGgKlhDdxSC72hooNpJZb\n+NfhSRyDoToCq9vcvxsDdUl3/UBPljTDwiZ37UChjO0uSB7V/7WlXxr+iTbZBW4F0RjIfuHDqiz9\n2wYK0ttrpNOTUnIrLCQAvqqBKRvBrTNk5gQ7Yst8EsQ3qvhwT3DrDJkP3LS5pT6ssNdrGDP4xuT3\nkZDBSgzMolf3M0Md8WU/LR1fYOsMmTlu2twynzvK0SQkn8nPbbPX+DrdMbML/EjQrPfC4bUSOyRp\neivaEW/LBxFCNeu2fsefcAiNy7cArZDBX77+8QU2dcgszF9jskssfOU9lMMJDpkFbvpcbb4cZhZZ\n+Me6v69/lMFptDAZSH3ow3h11/BOOXIr+l418W+VITc3Xw5znwawSvzO/q2yHWntZT4IG3KfBnFW\n+mg8rZq6g0eTfjpCy4UjSD/l/vbJh6I4S31Ez2jpc0xFP3uAzmUJG6wRBSMhAbCiBglAdL8sVpkh\nsm8WXw9590VFBTR47SNjoKkJwr2sSV69ClYsE6ZtP7TuP6p3OjKgVD+IyD7A7cCvgDuARYADvO29\nvgCOW5scuNMY8z1v3wTQw9KxdROdbLP6iQDRzR0yqwRf1BCoGFoX43Fl7ujA2Dg0pNyFwfEeHhjS\nnIaXFsOBm7tpCy1vcRsCw6FDJ7htjtb7QgSm2tjLLKy46fFxgaFdc7TMCmHFDe1PBwgWzL3OzPUR\nmDowFVeA8N4pGv9YQWjXNNnPAlgVDv4xxfOPHtJOw6WVWJUOrXfFiezn9vqGdk7T9niMwNRGsvMD\nZD4KEtymf2sYugrtlaLpygqSu6TIfRbAKnc6NTwKRQ5up+m3lViVNm33JAjPSCIhQ9MVFfgn1GKN\ncGi93d2+VvqVMMFdBuw0puqAVuZfPJbKfdpo+zREoNImvMm6zVGv2KONhX8YxSYn1+NkLFbeX8GW\nl65c79hC2+RofjBKcLJNboWFFeu5HIanZ2m6J4wVT9H2XJDwdm459FUZ2p4LENk5R2qOHwkbJAip\n9/xkPvNRdnSG1Lt+AhP6Xk4De6Rp+3M56elp7M/97u88uniDMnRQktbLy7EqHZL3xgjtmyKwQ4aq\nZ/PHqOWSCkKHtxPcOUN2TpD2mxNU/LW202M9+2OgzuXAZJvUawH849PYyyyyiyz83vFyWiH9pp8R\nv+z/wvu1Ro42fPShRfVIQ0uzEAxCpIcnt7W1wvvvWOyyh0Og7w+D2vgs7RMHHRlQqr92A94E7gUO\nLdjWBmxmjNm3YBrQs8AhIjLGawjMAbYeqEB8Yag5IUPDK37aF/oYe5xbmUp+YbHsjp4fAThYgj7Y\nf6L7yNCVbTBjU3f7qjb494Lu6T+phYzjrj+49yP31e7Vi1a2dV9Q/N/KikD5WUmSTwXcqQCnu5XR\n7GcWjddEuqUPTraJHpSldXYIKwyxI/KV1cw8X7+eGlQytrih6so6WmYlSL8TouqyOtLvB1nzw+pu\nacM7p0mc3ELjHyqQmKHsLPfpO2WnN+M0Wyw/sIba71dTeXED1gCNallxw4gr6mi7K0H63RCVv6sn\n836QurOruqUN7ZwhdlILTVdUIFGHxJnNSBjKL2yk7vsjWXPcaPwTcyQKnhqUeSdEcNrANFwA/GUO\n025fzJIbq2h8JcbWNy+h6c0Ic745oc95jNivjepDW3hj70m8tsskqg9qoWq/vk8RKsWKQOV3U7Q9\nGyQz30fFqe40mMznFvXXd+8uDk1yiH81Q/MDIayQIe4tUq84MUXqXT9rfhel/bUAFd928yk7Jk12\nicWqn0ZJvuOn/Ft9b2RZcUPisnqS98TIvhsifmkD2Q8CNP24+9MFAjtliJzYRuuV5UjUEDm9597+\n5F0xTLvVMWpQf3j/574M1LkcOzSDaRfqLojReHWExP+msbxR0vZ/hgh/JTsg51AgANN3dVjwmUVd\nrbDjzm4Dq74O3nyte9Vx0UIhm4W3X7d46kn3lVr/dfVqkIgxQ6sHUakvAxGZCDwGjALuA04B9gKe\nxB1xawXeAs4wxjSLyGnAxd57fzbGXNlT/lMuax/yJ+bef+t+AxtK/jJn6Pd1jL51/R9JuSGFdm3s\nPdFGZjtDe4ho6qZD/xh+8reJGzuEXmWnD+1zxZqT6D3RRrbrP4Z+L8pjD/sG94R+8KTBu9d+fdaQ\nvVgN/bulUkOQMWaRMWaaMWa0MeZsY0wCmAHcaIwZDUwFosB+Xvq/GGM2M8aM760hoJRSSik1WLQx\noNTAeQE4RkSWAwtwpww9s3FDUkoppVRRljV4ryFMFxArNUCMMXOAnTd2HEoppZRSfaWNAaWUUkop\nNfwMh8fS9cHQHrdQSimllFJKbTA6MqCUUkoppYafQX540VClIwNKKaWUUkoNU9oYUEoppZRSapjS\naUJKKaWUUmr4GeKP/BwsehSUUkoppZQapnRkQCmllFJKDTvOID5adCj3vg/l2JRSSimllFIbkI4M\nKKWUUkqpYccZxDUDQ7n3fSjHppRSSimllNqAdGRAKaWUUkoNO4O5ZmAo05EBpZRSSimlhikdGVBK\nKaWUUsOO7dM+cdCRAaWUUkoppYYtHRlQSimllFLDjq4ZcGljQKkh6GsXvrmxQ+hV6/nBjR1Cj0bf\n0byxQ+jVqu+WbewQehR7fuXGDqFXjjO0b+aLQomNHUKvZnzn440dQq+e+mDcxg6hR6ed/urGDqFX\nd+269cYOoQ/Gb+wAhiVtDCillFJKqWHHDOL/MzCU6VFQSimllFJqmNKRAaWUUkopNezomgGXjgwo\npZRSSik1TGljQCmllFJKqWFKpwkppZRSSqlhR6cJuXRkQCmllFJKqWFKRwaUUkoppdSw4+ijRQEd\nGVBKKaWUUmrY0pEBpZRSSik17OiaAZeODCillFJKKTVM6ciAUkoppZQadmzRPnHQkQGllFJKKaWG\nLR0ZUEoppZRSw46uGXDpyIBSSimllFLDlI4MKKWUUkqpYUdHBlw6MqCUUkoppdQwpSMDSimllFJq\n2DH6PxADOjKglFJKKaXUsKWNAaW+xObeVMNfavbkb7vuTNPCcMl0dkZ48vituSH6FW7ffHcWP10J\nwONHb8tfavbseF0f/grLXiwf0Bg/vWU0s8dP59Hdt6NlYajHGJ/71mTuTOzO/ZN2Yvkz3eN48Ttb\nMv/OkQMWW/L5ALXnxaj/TRR7Tc9zR40DzXeGWPPjGE03hTFZd3v9pVFqz4t1vFafEceuk459Gq+K\nkPmPb8Bi7sndnMCp/HVQPgsg+0iE9mOqSZ0xAmdF6duJsSF9RYL2o0aS/lU5JlPwngOpCyqw3wvk\nt9VZtB84ivZjqmk/ppr0zL6XydyjYVJfryJ9VmWvMWX/mCB1TDWZmWUdMZkkZH5RTup/qsn+Od6R\n3n4/QPqMSlLfqCJ7VaIjvbPcIn1mJamvV5O5sBxTv25zkJtnx/lin3Es/cYYsktLD9YbG9b8cgSL\n9hrHqnOrOz5/6dfH8MU+4zpeC7bdlNzyfHlrfTzKitNGrVNMpcy/dSQPT9iBf+25Da0LgyXT2Rnh\n5RO34G/l03l08vasfKasx+3rK/lglLqDR9N4cjX2stLnmrGh5bfl1B00muaLKjuVw7Uaz6oi807+\nuxkHms4e0Wnb+nj3pnFcM2YGt03fncaFkR7Tfnj3WO796nQe/sb21M+PdnrvidO34YPbawYkJoDW\n+2Is27+Gld8aTW5pz8ew/pJKls2oofb8qm7HsO2JKKvPLH6Nrrt4BG2PRIu+t7E4lgzaayjrtTEg\nIjNF5IOCfz8vIqesy4eIyO3ruk8PeX1XRJaLyEoR+UGX93YTkU9FZLWIXCAie3rpmkQk6f395wXp\njxeRq9czni1F5G8i8oyInFSw/WgRWSwin4jITn3I5xMvvpUistTbFhSRm0VkqYjMFZF9vO0zRaRV\nRBpF5DkR2XZ9vkORWE7xjtcq71j/bIDzH5D4B6hshkQkKyKWiLwtIieIyLkicl1/YhpMtXNjvPXb\nCRz/9tt85er5vHD2pJJp3/vTeKyA4bTlr7LbzIU8/wM37eF//5DTlr/Kactf5cQP36RssxSjd20Z\nsBjr50aZ87txHPnGHHb/8wJeP3fzkmk/uqoGK2A4fslb7PjLJbx6due0y58r4/O7BqZSA5BbatH2\njyCVv2gn8a0ULfeWbkwBJF8IYNdaVF/Rhn9Th/Zn3MrBiF+0U31lG9VXtlFxfhL/pg6+KuNWPO4M\nk5k3OA2BxzmMH3A9hsG56TgL/ORmxQjfWk/gnBYyV5eu2OUejWBW+og8sAZrcpbcg26FwOQgc0UZ\nzvudK1rOZ378ByeJPlxL9OFaQjOb+hiTj9ysGKGbGwic3ULumkTJtPZjEcwqH6H7arEm57AfcmPK\n3RFD4g6hB2rBCPbzIUwWcleUEfhpC6HZdZhmwb7fS39LHP+ZrYQeqEW2yJGbFetTrACZeQEabipn\nkwdXUP3zemp/U1kybfPf4uSW+Znw3FKCW2domuUe73EPrmTCS0uZ8NJSxt6xktDWGfw1tns82oS6\nP1VSdVFDn2MqpfHDCB9dtglfe+0jdv7TF7zz44kl03765zFYfsMxX7zHtF8u5a0fTexx+/rIfeYn\n+dcEFbPWEDu/idY/lm44ph6K4qzwM+Ifq/BPzZKcHe/8/qMRcu/mOyxMDlp/W0H23dKdGOti9dw4\nr1y6Bae++xoHXvsJ//7hViXTLn2lgreumsCB137C+K808NjJ0zree/GXW/LhrIFrCGTmB2i+pYwx\ns1dSeVEDDZeXLoet98fJLfdT89RygltlaLk7f445bULT1eVUnN/Ybb/UGyHaH+v7uaEGV19HBqaJ\nyB4bNJI+EJEpwO+BGcCewKUiUng2XQT81hgzCvirMeZVY8wY4BzgEWPMGGPMb9cmNsb8zRhzznrE\n4wMeAv4GXAD8TkSmisgo4P8BhwCHA7f0kk/UDceM8V7jvLfOAGLAROBU4D4RWXvmXQeMBJ4FnhCR\ngbla5T1ijBkNbAd8W0T27E8mIlIhIucWeWug4l+nsikiEwsbDMaYNFAHjAA29V6jgCX9jGfQLHik\nmqknrSRek2HsHs2k6gJk24qf0tXbtTLj+nmEKnJsdngdrUu7H+73rx7HtO8twx92BizGxY+OYIsT\n1xCtyTJq91ZStf6SMVZu18Ye1y4gVGEz/rAG2pfmK4i5lPDGuZsz4ei6AYst/b6f8B5ZfBWGwBYO\nTqtg0j2kf9dP9KAM4ofIjAyZ97v34rY/ESR2uNtVlvnEh2+kQ2Bze8Bi7sltnMovuHRQPgvAfjmE\n76AUVrWDb5ssNFmYZIm0L4UIHN+OBMB/VBL7Fbf8Oe8EsTbJYW2d7ZTe+cyPbJlb55icV9yYpNrB\n2iaH6SEm56UQvuPcmHxHJrFfCXrbw/i+1Y5Y4DuyHfvlEM7cAFJjY22ZQ3zg2y+N85E7kmG+8GNt\nk0UErO2ymNq+N/7anomSOLIN/yib8A4ZnAYfTnvxxlzbU1HKT21GglD+rRbanu3eq9x4SzkVZ+Yb\nTg3XVRDdN0lwUrZb2nW19NFKJp5QS7QmS/XuraTr/ORKnsvtTL/mC4IVNjWHNpJcFuxx+/rIvBAm\ndGi7e65tl8U0Wphk8WOYeS5C5MRWJADhY9vIvJjvAHDqLdpvTxDYPdWxLftWCN+4HP5pRYYQ+mHe\n30cx7eTlJGrSjNujifbaAJm24uUl1eTn4Js/pnqrNrY7dRn189zGZ+3HMZL1AbY6fsWAxASQfDZC\n9Ih2fKMcQttncBqskuUw+UyExMktSADix7WSfD5fDptuKCe8T6pbeTNpaLi8ksgB7QMWsxpYfW0M\nNANnbshA+uhw4F/GmHnGmAXAP4HDCt6vxKvEGWMGrtZQWjVwnTHmIWPMu8CbwCTga8BTxpiPjDGf\nA4tFZHIP+WwPzC2y/XjgRmNMzhjzNlALTFn7pjEma4y5FMgB+w3MV+rMGFMLPA7s088sKoBijYGB\nin9dy+ZE4JQu25bgNgJW8yVqDLQuDVE9ra3j37GxaVq+KN67PfHQekLlbqV0ydOVjNm9udP7dkb4\nzz2j2fo7Kwc0xrZlQUZMy98AomMztH1RvN03/pBGgl6My58pZ+RurR3vzfnteCYcU0fltgN3M7Eb\nBP8m+YaPVe5g15W+JDoNFv5xbnorCk5L55ul0yxkF1kEt3crscFtbGKHDUwloi8e4FiqGIzLnsus\nsbC2yFfYpcrGrCpesTFrfB1pJW4wje5xtnbNEPjf7r+pMz9AbnaU9qNHkvpeJc6SvlWwzRof1uaF\nMTk9xGR1pJW4gSY3JtMsyHi3HMpoB+NNOzFN+d/bWeCHcrcsyGY57McjmDbBfjKMNb3vv3lupY/g\nlHx63yib3PLiU4XsVX6CU9xKlpUwOPWdv1eu1iI9N0R0f7f1k1nop+nuBLklfladX036w/WreLcv\nC1JRcC5HxmZoW1w8z5qDmzrO5VXPllO1a2uP29eHs9qHv6DhaFXb2CuK/+bOah9+r6JqxQ1OQ/58\nb7uqjOgpLVhV+WtCYPc00VPXP8a1WpaGGbldfuQ1UZOmucQ1e8tDaxm7czPZpMUrl27B1GNXAVC1\nVRtfu+4TZAAnedurfAQm5yvwvpGlj6G9ykdwsltm3XLoBpJd5Kd1dpzcEj91F44g81F+2l/TLeVE\nv9pOYAAapQPNsaxBew1lfY3ufuAoEekYOxKR2wv+/ryIzBCRRSJyj4isEJHLvOk6J3vJDhCR+d40\nnukF+/7Mm07zhYgcUbB9kYjs700jucPbvDnwRUFci4GJInKUiKzEHS14yJtqM4VeeNNhCr/H7SLy\nuIgsE5ErRGShiNzivXeoF/sqEZkJYIxZZYy5RVz74lbqXwTGAR8UfNQXwJY9hLIjsLc3JedzETmy\n4L1q7/MTwATcBkFXc4CpXrpLvPgXr522JCJnishtBd/zYRH5RkH65d5v9v1ShwpwSuVfKh8RuQd4\nCxjv/Sb/LJH/HGBqkd/jeRGZ4f39VBFZ4OX/3YJ9u5VNL/0FXoz/EZFDvG0v4Y7krJ0+dquXfAkw\nHXgBqMFtDCz2pg5d6X3fOSKyS0H+RkR28qYWXepts0TkFi/GxSJyTEH6ouV8fRhbCJTlb4KBmEO6\nsecHhNlp4Y1LJrLzBZ3bOp8/OJJND6wnGB/YXmw3xnye/phDuqn3GN/79Ximnb8MgIYPoyx9spLt\nf7a033E0XhfuNK+/9rwY2IJETEcaCYHpqa3hgFWQ3nQZQEm+HCC8Zw7x6owyyFNEB3tGqrEFiRYc\nhLDBtJa4pdhAYVqvSJQ6RjLWJnRJE9G/r8G3b5rM9aWn+3TiALH8b0TEQGuJD3G6xuSlC5l8XBED\nbYK1bRbTYpG5PEH25hj2vVF8+7rDSP6jk+Rmxcj8vByz1I9vvxR95riV0rWsiIPTUvwYGhusmNPp\n34VaHoqTOKa1I/am28sIbJqj7JsthKZkWHHa6B4bu70xNp3OZV/UIdOH683cSzdhq/NX9Gl7f+OS\nguMiEYMp8ZsbB6SwfHhfJ/N6CKfBR/jIzsNIA30OG1sIdbpm26QaAz3sAY+fPI23r9mUHc9cskFi\nAtxy2OUYliqHdDmGxjtvWu5M4B+fI35cK4HJWVafOQq7ziIzP0DqpTBlZzQXz08NCX19tOja3uGT\neksIPAEEcaeA/Bp3Sg+4ldVpuFNnbsOd3nEIcACwFW4F+nkR2dSYtUvz+CPwI9zKIkCYzpXhDBAx\nxjwCPCIizwMzjTHP9/F7FbMSuAw4GzgGt3ExErgWt/e6AXhdRB4xxrzn7XMccCtwszGmSdzpQ4WT\nNNuAChF5BNity+f9FfgUuNAYc5eI7O59l38BdwPXichUL5ZPjTGLpPvVoBWIi8imuD34k4Fy4F1g\nFvAw8GsvrgCwF3CiiIwA/g8YjdswvAW4oTBjERkHHAkcXyr/UvkYY04QkYnA88aYiT0c81YgDnSf\naOjGsA3wE9wKewB4V0Qe9d7uVjZF5KvAt3EbZ2OBZ0Vke2PMPl7jYqYxZkbBRyz28v4Et8FVjdtA\n+A6wA7AFbkPzfhGZ4k0tArgCOA2Y7/17B9zRqwnePmcDD/ehnK+N+wzcqWHsd+NP2PaMntsMocps\np5txLmn12lv0+q82o3r7ViYcXN9p+6d3jWbnCxf3vHM/hCpzZBrzPUx20kIs08Me8O7MTRmxfRvj\nvtaIMfDa2Zuzx/Wf4wv2vF9PKn7YvYLWen8IUzgUnqXHGrVEDU674AuZfPoCqdf9VJxTYk7KfyFJ\nOJ0r/xkpefwk4TYUJOLk0/YgeGa+N9Z/dDvZ2+I9pC4QdzpX/tOU7vJKGGi1wIupYyGkgDFepSvt\nficJQujaBuwnw9jvBpHxNtZuGYzjLkIO3VSPjHFw5gTIXl5G8Hd9W+NglTnYzfkAnbRAifPDV+42\nFKyoW4M16c7HsPWxGGNvXp3/6h+EGPHjBmIHJIEk7a9ESL4VJn5w/0bXgpU22YLrjZ3q/Vye++tN\nqNiunZqDmvq0vT+sMoNTUA5NunQ5tBIOTovgW9uozwgmBW3XlFF2RX3xnQZQuDLbqfKfS/p6PYbH\n3D+HhU9V8fCxO3DW5y9tkMaAVda5EWrSUvK8kTLTuRx653JmbpCKcxqJeI3h1Kth0u+EaLknQeXF\nDUjPbZ6NxhnsXpshal26CW6i9HSMwqP5Gm7lbu2faz/jLmNMyhjzMLC5iJQBXwV2AT7H7ZWN4fbM\nrvUHY8zLxpi142rtuA2CtULetoG0Nu43cSunFrA7sIm3bb4X4zZrdzDG/A13eskhXmWzAXd6zFoR\nwDHGHFWwLmDt62fGmDuNMXd5eb2OO5KwvTHmJtzG0EhgJ9z1EsXEgFZjzGLcKTnn4TYkRnt5rsat\n6O4D7A+8YIxpB5qA/wBXAQfiVqDXWjva8gJwlTHmnVL595JPX8Rwj3lXa8vV/rijQh/jNgzjFEyX\nonvZPAS3vDUYYz4G3qDnaU5rRwa+AAzu+oHlXj63euX2WdzvOa1gv58bY+Z4xxLccuzgNhImk58e\n1Vs5B8AYc4sxZroxZnpvDQGAUTu3sOL1cm9fWPNenNgmpSe9L3pyBJ89MJL9bpjfaXu6yUftB3HG\n7rX+N+auqnZqZfUbiY4Y696PEa0pPY1i6T8rWPRQFXtctwCAtiVB6j+I8uyxU5k9fjofXlXDGz/Z\njLl/Wv/Fc/6JNtkFvo7Ysl/4sCpL35gDBentNYIUzJDIrbDcuedV/W+wfNlYU7L5efMGnHkBZGTx\nkaXCtM4KC0Klj5PJQu5f+cu8qfWBZTB9OLTWlFynmMz8AFJdfA2MNTmL83E+JvFisrbIYea5lV7n\nP35kpDcdqNrBf1I7JAX/KV4PfIOFabSQMV6ayVmc//S91hPaNkN6TrAj3szHIfyjih/D4DYZUu+7\nU+yyS/1IOH9Aqkd3YwAAIABJREFUMp/7kaDpWDgMbk+vf2zBlKmgwSrr/3qgETu1UftGvCPWhvdi\nRGpKT/tY/q9yFj84gl2uXdSn7f3l3ypDbm7+GOY+DWCVOIb+rbIdae1lPggbcp8GcVb6aDytmrqD\nR5N+OkLLhSNIP9XzAwX6Y8z0Zpa9VtER68p3EyRKXLOXvFRB22o31s0OrCNZHyDbvmEeRhDcOkOm\nsBx+EsRXqhxunSHzgZs2t9SH5a0xk5jBNya/j4QMJiVk5wWoPbeaZfvX0HJHgoY/VNJ8Rx9H+tSg\n6fN/OmaMeUVEbNwpLbd3eXuTgr/bXf7syKLL3y3cyt5vjTG/B3exKZ0rha93yWMBbsV8rQm4FfSB\nVCx+AZ4zxqydbhIDHBEZC0w0xrxmjGkUkadxGwlvA78o2H86bu98UeJOpZptTEff1CZ403KMMQ+L\nSA63Z/mhEllMA24U92lDtwO/Au4AFhWkeQA4Grcxdb+Xt+1Nffkq7tqLS71eeHAXEH+zS5xF8y+V\nT8H36c004EY6lyMK/i3AncaY73lxJHD7/Pb3Pr9Y2exa3nqqSizx9l2E2wCMGmOy3ghMyXy8hhsF\n/24Ska2Bg4ATcEcz9qX3ct4vEw+p5+Xzt2Tcfg3UfxQjXJUjMb74jaXxswhPnTKVIx6bS6ii88LM\nJc+4awisDXCfGXdwI2/9dCJjZzTR+FGU0Igc8fHFi0XzZ2Fe/M4kDnzkE0IV7ukX3zTD/9blT/H3\nfj2e+MQUk05es96xBbfN0XpfiMBUG3uZhRU3+EaULiahXXO0zAphxQ3tTwcI7pg/jpm5PgJTB2eh\n8FDh2y1D9oYEuZ0ymIV+pMzBGlW8suk7IEXmyjKkwiH3QBTf3qUbrRKA7F0xZJS7YDd7ZwzfXuk+\n9Yhau2bI3hDH2jGLWeSDMgcpFdP+abJXJTpisvbK5LdfHyfw3TZyf4njOyA/qmS/FQQLfHt7ZbjM\nAQdyT4Sxts1iPxbBmtj3hc/RfZLU/b6SyG4pMvODWBU2/rHFy1H8sDZqZ1bhG2HTdGcZsYLFmO0v\nRYjs1nn0Kzw9TcujcYJbNZCZFyD9YZDQNv1fw1LztUbeu3BTRs9opumjCKGqHLES53LL5yFeP21z\n9n14HsEKu9ft6yOwR5q2P5eTnp7G/tyPVe7gG138Nw8dlKT18nKsSofkvTFC+6YI7JCh6tn8WqmW\nSyoIHd5OcOeBX++zxSFreOYnU5i4fx1rPkwQqcpSNr74tLIvnqvi3ZvGc8Sdc/ni2SoSm6QJxjbM\nNSa8d4rGP1YQ2jVN9rMAVoWDf0zxz4oe0k7DpZVYlQ6td8WJ7OeOhoZ2TtP2eIzA1Eay8wNkPgpS\ndXkdsSPz5bTpxjL8NTliRw2dhcRD/ZGfg2VdJxDeCJTh9pyOB3cuPe6UiN58S9zHOB4BLDPGNAJP\nA8eJSJmI1OD2nFb0kMfjwEEiMkVENsetdP1jHb9Df7wO7Oh9bhB4yvvsMO40onHeSMdBuI2TN4FJ\nInK6N/VjS3putHwFOE9EouI+LjVD5wXF/wf8zJjOfWMi4heRi3Arm8/jTkF6E7gXOLTLZzyEO91n\nf7xjJu6i5me814XAGKCqhziL5t9LPnVAlffdoiISKdiva/zNFC9Xz+KOuozxGgJzgK27xLa2bAI8\niTsNqsKbYrUb8LL3Xi0wTkR8IlLpTZ1aOz/mC++1pCCf07xyuy9u2fyw1MERkQNwp339Hfc321Xc\nFsW6lvM+CZbZHHLfR7z/5/Ese7GCg2Z9zIpXy3j08Gnd0s69oYZMk59/HD2t4/8UaFvh9u4sf7Gi\n24LigRIss9lv9n/46OoaVr5Uxr53zGPVawmeOqr7I/U+uWkM2SYfz/yPOwowe/x02ldsuLFlKwLl\nZyVJPhUgM89H2enuTS37mUXjNd2f1BKcbBM9KEvr7BBWGGJH5Cu0mXm+QXtq0FAhMUPwkiZy90Wx\n5wQIXtyE/WGA1EXdi7Zv+yyB49vIXJuAqCFwSluRHPOCFzaT+WMZyROrwYHgj/r2uFuJGYIzm8g9\nEMGZEyTws2acD/1k/q/74yat7bP4jmsne10cog7+b7sx+Q5K4ds9Q/bPCaypWXwF88hzt8Xwn5Fv\nx0sAAhc2Y98TJXPGCJy5Afzn9P3RvFbcMPrPa2i8o4zkWyFG/aGW1HshVpzV/Tntkelpyk9tpu53\nI7BiDpU/yI/kpd4KE9q+c+W14swmnGaLxTM2YeWZo6j+VT2+8v6PDATKHPa+5zM+vXoMq19KsMft\nn7PmtTgvHN392RjzbxpNtsnHi8dO4uEJO/DwhB1IrgiU3L4+rLghcVk9yXtiZN8NEb+0gewHAZp+\nPKL7d9gpQ+TENlqvLEeihsjpA/cY5b4Ildkc88D7vPmniSx+oZIj7/6Apa9WcN9h3Z8+vvtPF2Js\n4dqaGbzw80kcNXtOkRwHhhU3VF1ZR8usBOl3QlRdVkf6/SBrfljdLW145zSJk1to/EMFEjOUneXe\nO8pOb8Zptlh+YA2136+m8uIGrLLhM1L6ZSeml7FXcRfLho0xF3kVseW4Fb4TcJ8C8yHu3Oxf4PbK\nzgBm4lbuIL9mYATuYyozwCnGmFe9/H8BfBe3J/7nxph7vO2LgBnGmEVd4jkduBS3ITPTGHNjwXvP\nU2TNgLiPkjy4SE/3Kd5nnOL9+/Yucc/Em+8uIocDVwIJ4F5jzHkFeVyKNz3EGHOdt30qcA1uBfXc\nrr3IXeIYhdtbvxPuPPwfGmPmeu8dCvy0cI6795ucjztr+XUv/efe/PzHcBfA3of71JydjTHzvP1e\nAVYYY44tyOty8k/Xuc0Y8389HK+S+RfLp2C/i4Ef4P5mewAnl4g/CvybLuXKGPO8iJwGXIw7mvVn\nY8yVJcrm2caY20XkAtw5+yngHGPMkwXx3IQ7SmLjNtRG4K7HSHjl6yBjzHEiYuFO+fkmbiPidGPM\nW14exhjTqUtBRPy4a0cO9b7D5caYa733ipbzUn7kPD/kr6Kt9sD8Jzwbyj/u6N4oGmpWfXdg/tOl\nDSX2/MA+XWpDcJyh3bNXM2boryPZbfNVGzuEXj31wbjeE21E392l2AMBh5a7Pujahzb0LN5u/KCe\n0PPm/3LQ7rWTJ/16yF6sem0MqP8OXkX1RuBpb42DGsK0MbD+tDGw/rQxsP60MTAwtDGw/rQx0J02\nBlx9XjOgvvRW4i4i/uHGDkQppZRSamOz9WlCgDYGhg1jTPfJf0oppZRSaljTxoBSSimllBp2hvr/\nDDxY9CgopZRSSik1TOnIgFJKKaWUGnaMrhkAdGRAKaWUUkqpYUtHBpRSSiml1LCj/wOxS0cGlFJK\nKaWUGqa0MaCUUkoppdQwpdOElFJKKaXUsOOI9omDjgwopZRSSik1bOnIgFJKKaWUGnZ0AbFLRwaU\nUkoppZQapnRkQCmllFJKDTuO/qdjgI4MKKWUUkopNWzpyIBSSimllBp2bEv7xEFHBpRSSimllBq2\ndGRAKaWUUkoNO7pmwKWNAaWGoL+8tNXGDqFXdnZoDyyO2rVxY4fQq9jzKzd2CD1qmzFmY4fQq0sw\nGzuEHt316aKNHUKv7n9hs40dQq8O2XPpxg6hR1c9t+PGDqFXE19NbOwQerfdxg5geNLGgFJKKaWU\nGnZ0ZMA1tLv2lFJKKaWU+i8nIn8RkddE5OIS7/tFZLGIPO+9pnnbLxGRt0Tk+v5+tjYGlFJKKaXU\nsGMsa9BePRGR/wF8xpg9gM1FZFKRZNsB9xpjZnivuSKyM7A3sCuwWkS+2p/joI0BpZRSSimlNiAR\nOUNE3i54nVHw9gzgPu/v/8at4He1O3C4iLzpjSL4gX2BB40xBvgXsE9/YtM1A0oppZRSatgZzDUD\nxphbgFtKvB0Dlnl/rwd2KpLmLeCrxpgVInIncKi33+cF+43uT2zaGFBKKaWUUmrjaQUi3t/jFJ+5\n84ExJu39/W1gUh/365VOE1JKKaWUUmrjeYf81KDtgUVF0swSke1FxAccDczp43690pEBpZRSSik1\n7AyhR4v+HXhJRGqAQ4BvishvjDGFTxb6NXAPIMCjxpinRcQCLhORq4GDvdc608aAUkoppZRSG4kx\npllEZgAHAn8wxqzE7fkvTPMhXf5bNmOM4z1B6DDgamPMwv58vjYGlFJKKaXUsDOERgYwxjSQf6LQ\nuuyXBB5Yn8/WNQNKKaWUUkoNUzoyoJRSSimlhh1HtE8cdGRAKaWUUkqpYUtHBpRSSiml1LAzlNYM\nbEw6MqCUUkoppdQwpSMDSimllFJq2LEtHRkAHRlQSimllFJq2NKRAaWUUkopNezo04RcehSUUkop\npZQapnRkQCmllFJKDTtGnyYE6MiAUl862UcitB9TTeqMETgrSp/Cxob0FQnajxpJ+lflmEzBew6k\nLqjAfi+Q35aF9Mxy2g8cRfL4auy3g+sUl/1YmPRxVWS+X4HpJa7sn+Kkj60i++uyjrhMErK/KiP9\njSqy18Tz+T4bIn1UFenj3FfuoUin/HL3RMleFWddtN4XY9n+Naz81mhyS309xlp/SSXLZtRQe35V\np2MI0PZElNVnjuy2X/3MStoeia5TTF1tsN+5zqL9wFG0H1NN+zHVpGeWr1ec6+JuTuBU/jpon1fM\nW8AVwM1AQx/SfwDcAdwH1HnbWoBLvXyu8N7rr6bZCRbuPZ4lXx9Ldmnp/jljw+pfVLFgj/GsOGdk\nx+9sN1osPWkMC/cez9JvjiGzMJ9H450Jlp08mlUXVpOrXffbvfN4mNw3q8j9sAKzsuf9jQ32VXFy\nx1Vh/6bgvE6BPbPM3X5t/jw1Ky1yZ1eQO7Sa3I8qMKvd/J3nQuSOqXI/95tVOF3O994s+ksV/95i\nW178yhTaF/V+DZvzg/EsuWtEx7+zTRZvnzyRf0/alpf2m0zLJ+F1+vxico+GSX29ivRZlb2ey9k/\nJkgdU01mZlm3cznz03Ls9wPd9sv+MUHun+sfZ1cNb/qYf3mYRTeEyNT3rdK84uEAje92vqaufDzA\nfy4JM/+yME1zSl9v1cajjQH1pSAiM0WkVUQaReQ5Edm2l7Qz+5H3ahFZJiLnrWesV4vI8euTRynO\nAj+5WTHCt9YTOKeFzNVlJdPmHo1gVvqIPLAGa3KW3INu5dTkIHNFGc77nW+Uufui4DdEHlpD4Dut\nZK5K9D2uhT5yd0cJ3tCA/4et5K4rXTl3Hg9jVvkI3luHTMpiP+ze7O1ZMYgbgn+rAwfsF0JuvAv8\n+M9pJXRfHaH76vD/T7IjL7Pawn4kjP+Utj7HmpkfoPmWMsbMXknlRQ00XF5ZMm3r/XFyy/3UPLWc\n4FYZWu7OHxOnTWi6upyK8xs77dN0fRltj8f6HE8xG/J3dj7z4z84SfThWqIP1xKa2bResfbV4xzG\nD7gew8briVsFvAicCRwKPNFL+sXAa17aCcDD3vaVwA7ABd7ruH7Gk/5PgPobyhn/4HKqL65nzaUj\nSqZtmp0gu8zPZi8sIbR1hsY73TLRcEs5ZUe3stnLS0gc2Ubt5W4eLY/FaH8pwshL6/CNsqn9Xem8\nizELfTj3RPFd34Dv+6041/fc4Db/CMMqH76765Ats5i/u+e1453Xvtnuee1457VzTQJr/zS+h2qR\nzXM4d3vldoEf60et+GfX4Z9dh1Vwvvem+aMw838/hq+8/CnbXrGEueeN6zH9p78Zw9LZnY/LvN+P\nIbZ5mgPnfcik81bx1gmb9fnzi3EW+MjNihG6uYHA2S3kril9XbUfi2BW+QjdV4s1OYf9kHdMbMj9\nMYEzp3vjJntbDPupgW8IpFYKdc8FmPj9FKMPz7Lq8e6NkK7WPO2n+f3Olf3mOT4yq4UtfpJik29m\nWPVoACc34OH2m4MM2mso08aA+jK5DhgJPAs8ISKhdc1ARCpE5NxieRtjRgF7AReKyPb9zAdjzDnG\nmL+ta2x9Yb8cwndQCqvawbdNFposTIl7pf1SiMDx7UgA/EclsV/xbsLvBLE2yWFtne2U3toiR/Dc\nFiRh8O2Rxqzpew+O80oI34FppNrB2jqHae4hrpdD+I914/IdmcJ5NZT/bt9sRyzwHZnEecW98Tmf\n+ZEtit89cjfE8R2TRCpNn2NNPhshekQ7vlEOoe0zOA0WTnvxC3XymQiJk1uQAMSPayX5fL6XsumG\ncsL7pAhOyh/H7Od+nCaL6Nfa+xxPMRvyd3Y+8yNbDv7d+DZO5RdcOuifW+hTYHugDBgPtAOZHtKn\ngCNwLzo7kB8ZWAmMGYB42p6JkjiqFf9om8iOaewGX8my2PZUlIpTm5AglJ/QTNszbkUx83mA8A5p\nAMK7pMitds9b48DIX9UTnJAjcXgr2UW9V+YKmVdDyFfTSJWDbJ3DNFmYVA/pXwkh3nktR6RwXgt1\nbLeOd89r64gk5tUgxoAclMI6JomEQaZnoNa73nxe+nzvzcrHyxn3rXrCY3OM2K2dTL2fXFvxak7L\np2GyDX5qvt65Mb/8oUomnlGLCIw5vInUygDp2v73ZjuvuOeyVDtY23jHscS57LwUwnfc2mtjEnvt\nNfCdILKJjXQ9lxf5oFmwZvTww/RT6yc+ynbMESiDyKYOdrvg9HCypFcLdlJITLM7bfeXG8b+TxZ/\nAqKbOW7cAx+uWk/aGFBfKsaYrDHmUiAH7NePLCqAopV4L/9FwOvAlPXJZ0MxayysghulVNmYVcVv\nVGaNryOtxA2m0T3drV0zBP63e2XVt3sGibuVavudYLdKZI9x1VrIZoVxOZjVJW6gtRayuXvDkJiB\nJq/y0yTIOG/7aAez3KvUfO4nd0kZ6a9Xkf1NoqNC4rwXwHklhPk4QPYPCcyyvl3O7FU+ApPz3803\n0sZeUTxWe5WP4GT3DmglDE69+xnZRX5aZ8fJLfFTd+EIMh+5FS3/5jkqf9a43lfWDfk7O/MD5GZH\naT96JKnvVeIsGZxh+wc4lqqO6vTG0QyMLvh3AmgskRZgMlADZHFHFLb2tq8AXgH+ANwK1PYzntxK\nP6Ep+bLoH5Ujt7z4VKHCtL6Ewa53f7fgpCzND8RxWoXm+xJE93RPkLKj2giMy2G3CA3/r5zYOjZQ\ni53TlCiD+fTFz2u88xrvvBYBa0Y6v+97QWQr97uZz/zYvy4j940q7N8memyAdJVcFqRs2/wO4TFZ\nkkuKTxWKT0kx7cqlIN07EjJ17m/Q/kUQkxX8CafvQXRh1viwNu9ybSx5LlsdaSVuoMk7l3fJ4D+x\n++8nE2wC57RukJpcrkkIj84fG3/CkG0s3bsdHGkYc0SWrh3g0YkOgQo3n+RSwRcx+NdtVqcaBLqA\nWH1ZzQGmishY4BdABPilMeZW7/1pIvIBUA6cZYx5UkTuAQ4ERojISuB9Y8zBhZmKyKbAdC9PROQS\n4HTABn5ujJnVWz4icjvwvDHm9oJtFwBnA0ngXGPMk/350sYWJFpwYwobTKvlhdeFDRSmtdfG0stn\nZCB7W5zgD1r6HpgNxApuqmEDrcU/yDjSKS5jS8c+HbF5+5skWHul8Z/aBlFD7rdl2I9F8H8jSW52\nFNk6i3VoCvN+gMwFFQTvrEd6u6o5YMXyny8Rg9NS4m7qeBWbLrG23JnAPz5H/LhWcov8rD5zFGMf\nWYGvqv+VhkIb8neWsTahE9qwJufIzo6SuT5B+PKeqsQDY7AHye8FlnbZNgkoHE4M4Pb+9+ZhYB7w\nHe/flcDeuA2FV4B/ASf2J0gHrHjnsmg3ly6LhWmN9zuXHdvC0m+OJTU3RG6lj01uX9Vpt+WnjSG7\n2E/VuaVXSNi/KsP8p/PIgeyShmjfzmk3E4GY0/nf3n6dzuu2znmYpT7MSyGsW+oxKZC90linuOe7\nc1kZ5rEI8o0+ThWywZ/InyO+mEO2sXjFu9T5Me64Bt79zgTGf6ueJfeMYPShTfhCfR957Mah87Ux\n0sNxdOhyLkuPsQ7U2teldwVJLu1c7uKTbaxwPm4raLBTAhQ/Fn2JZc2/Aoz4yhCaIwQ4uoAY0MaA\n+vJqxa3on4ZbeQ8A74rIo97704GdgIm4U4omGGNOEJGJuBX1iV3y+6GIfMfL8yJjzByvYbAPbgdh\nOfAuMKuXfLoRka8C38adoTAWeFZEtjfGrOqS7gzgDIDgeVfgP+Kk7nklHK9S6MlIyVqWJNwKpESc\nfNo+yN4Wx9oyh2+3niZQdP+sTje4dA9xxR1os6AjrrVv4E4fEG9/CyQCgXNbO/a1DknhPBKGbyQx\nn/oJ/KkRawsbds9gvxLCfO5HpvR8s7HKnE6Vf+N9VtFYy9yGghV1KxjGO4aZuUEqzmkksp9blUy9\nGib9TojoQX2f39yTDfk7B8/MH0//0e1kb/vv7Kb7VpFt/6Zz5T9H3xopxwGf4y4UPge3J2CtXYDn\n+hljt7KYEko99nxt2o6ymHYjX31xNWNvXE1kxzSZL/ysOHskmz68omO/8fetoOm+OKsuGsm4WSuL\n5u27pLnbNvvWWOeKew/nCQAJB1otCPdyXhdkaWywr0xgfbsN8XqPfefky6ccnMI86p7vfRGosMk2\n5Sv/dtJCrHWryE+duZz4lBQNb0dp/U+EHW/5Yp327ybudLk2Uvo4Jox7DL1zuesDCzaUcf/b/YNW\n/9OPnczH7WQFKTKK0lcNb/gwNlRML9KhoTY6nSakvqxiuP0omwMf444UxMlP73nIGFNnjHkHd6rv\n5F7yuw634dAK/APAGLMYdyrQecDddJ5hsC4OAe4yxjQYYz4G3sBtZHRijLnFGDPdGDO9WEMAwJqS\nxfGmpBgDzrwAMrL4xbUwrbPCgj70btlvBLGfDxH8cffKQU9kcg7n44K4PvMj1cV7yd20bj+EWWGB\nt/ZNNrcx87zt8/xQ7WBWWTjvFPRY1ln5q1bMIKMKelUDpnMPXAnBrTNkvIV4xkDmkyC+UcWPYXDr\nDJkP3LS5pT4sr6IjMYNvTH4fCRms9ZhK0NWG+p1NFnL/yi82NLU+sAxmPTo+v0xqgCXe3w3udJ/S\nS7PhC9wLAsAWuMN6aeD9gjQtuEWyP4cwtG2a1Hve3HoD6Y+D+EYXb8wWps0u9WNF3E9MzQkR3NKt\nzAUn5Mit9OOkhLYXIjgptzIXP7idzGfrtmZAJuUwBee0+cwPPYx8yaQc5hPv/F1pdQzByGY2zM+f\n14XXBeeOGFLuYB3mNtG6ne+11joNKZXv1E7Dm7GOmJvmRAjX9H26I4BYMP7EehJTUow+rJGKndav\ngW9NyXU6l838QMlrozU523EddVZYyPqMSKyn8CaG5BL3YmsMpJcL/r4/U6KT1Aqh9rkAY4/Nlmzs\nbiyOWIP2GsqGdnRKlTYNdwrwncaYMcaYMcA43Pn+0Pne7NCHsm6MaQf+CnwfQET2wZ0hsAA4ZT3j\nNV3+3q+rvG+3DPbLIXIvhsjdGUPKHKxRxW8svgNSZP8ax34/QPb6BL6900XTreUs9ZH+XTnBXza5\nPf3rwNolg/NKEPulIPZdUSThdKqod4prvzT27TGcOQFyN8fx7Zn2tqfI3RjH+dBP7rZYx/bsNQnM\nast7clAEa09vDv+0bMdTNJy3A26v6tjee53Ce6dIPhuh/ekIzTeXYVU4+McU3y96SDtN15eTejtE\n4x8riOznVgxCO6dpezzmNibmBch8FCS4zcB1422o31kCkL0rhv1eANMiZO+M4dsrPWDTDYa6LXEX\nEX8MvIA7t7CnB6suxJ0C5OCODJThtl1f8t5LevlMoX/ToGJfSdL6dJTWf0dpuKEcX4VDoEQZThzW\nRt01FSTfDFF7eSWxA9w55IFxOZruLCOzwE/D/yvDijtYYUPrv6LUX+9+u9Z/Rglv1/P535XsksG8\nGsR5OYi5Owplpc9pAGu/NM4dMcwHAZyb44h3/sp+Keyb4pgP/Ti3xzq2O8+HMC+EsM7rPB3RuTZ/\nvjuPRpA9+n5ejTqwmZWPlbPikXLm/34MwRE2kXHr1hgAcDLCghtGMfWXK3pP3AtrV/dctl8MYc/q\n+Tj69k+Tu827Nt6QwNprkIYGiohNsmn92EfLRxZ1z/nxRemY+78u7HZYOivImCMzBEcMk16HLyFt\nDKgvFRHxi8hFuPfeZ4FDRGSMiCRwRwfWrvE7SkQqvacCjcad8gvuKEGViES9V9eHWF8HnCQiMWA3\n4E3c6ceHdknXWz6FngRO9J5ANNXL9+V+ff+YIXhJE7n7othzAgQvbsL+MEDqoopuaX3bZwkc30bm\n2gREDYFeHr+Z+3sE2oT0zyo6nkFv6vp2iZCYIfDLZuwHozgfBPD/XwvOR36yP+/e72ptl8X3jSS5\nG+IQMfhOcis1vgPTWLulyV2dwJqcxToihYx28H+jncxZlWTOrcDaM411sNuL6P9eK+b9gPv/Elyb\nIHBRC9KHtbBW3FB1ZR0tsxKk3wlRdVkd6feDrPlhdbe04Z3TJE5uofEPFUjMUHaWO2JSdnozTrPF\n8gNrqP1+NZUXN2CVDdyNbkP+zsELm8n8sYzkidXgQPBH67A25EsujDvl5zXcXv+ve9sX4w79dbUX\nbkPgSuAZ4Fhv+1HAY8A1uK36Q/oZjxU3jLl6DY23l5F8K8zoK9aQfDfE8jNHdUsb2SVN5XeaWfO7\nKqyYYcQP3HUeo35TS+s/Yyw5pobmh+KM/r27nLn6ggbSH4dYuOd4Wp+IMXLmui3elpjBd3EzzkNR\n/j979x0mVXU+cPz73jt9+9I72MBCUbE3YoslscUSNRJs0USNMSbRX5pYYo0lamJJDMQeG8bexQKo\nKIigIigdqcv2nXrv+f1xh+0zW1jYTeb9PM88MHfOnHnnzt5zzznvuTNmvh/7Cu/vxHzuw/lDy+Na\nRiexTo7i3OMd11b64nXr8DjW3nGcuwqQkUnke97x6z4RhgoL5/xS7zcFflGM9HOxTqnD+VkJzmXF\nyP5x5Kj2X0HsL3TZ86GlfHN3X8rez2ePfyxj0wd5fPiD7Tr03lf8qxel+9VQuMuWf+2N5BkCkytJ\nPRXGnRcwTZ2lAAAgAElEQVTA/9sq3AU+Ev/XchhqjU1in1pH8u58iLj4ftz+r0zuanYIBp2eYNMM\nH3XLLAac4g1M6lZYrHyw/b9BU/mpTapaWPtcgMU3hlh8Y4joqp4z++CKbLNbTyYmV/LD6r9a+ncD\nfoX3xR4fABcbY74RkXOB3+Nd/3KHMebWdNn98Zb9hIFLjDHPNqrr98BFeIPh/YCJQMgYc2X68Wl4\nHfjX8M75ffGWC08C9jTGLGqtHmPMkvT2qWS+gDgGXNrWBcR576zr8Qemk+zZcwl9+/b8768rK+/Y\nD7tta7UTuuILNLeuqzuXZNtmHl64rLtDaNOy5Vv2uxjbwtH7N78cvGd5ffag7g6hTcNndnKdzzb0\n5e/C27TX/FT0H9usATk5fF6PHRHoYECpHkgHA1tOBwNbTgcDW04HA11DBwNbTgcDLf079sA2a0BO\nC53bYwcDPftsrpRSSimllNpq9KtFlVJKKaVUzunpa/m3Fc0MKKWUUkoplaM0M6CUUkoppXKOq3Pi\ngGYGlFJKKaWUylmaGVBKKaWUUjnH6DUDgGYGlFJKKaWUylmaGVBKKaWUUjlHv03Io5kBpZRSSiml\ncpQOBpRSSimllMpRukxIKaWUUkrlHBddJgSaGVBKKaWUUipnaWZAKaWUUkrlHFd0Thw0M6CUUkop\npVTO0syAUkoppZTKOXrNgEczA0oppZRSSuUozQwopZRSSqmcoz865tHMgFJKKaWUUjlKMwNK9UCB\nFaHuDqFNgXjPnktwese7O4Q2uW7PnpW6GtPdIbTpqh6+5jc+8pXuDqFN936wX3eH0KZTA591dwhZ\nvb5peHeH0KbdfzW/u0Noh7236as5Pbz92FZ69tlcKaWUUkoptdVoZkAppZRSSuUcvWbAo5kBpZRS\nSimlcpRmBpRSSimlVM4xes0AoJkBpZRSSimlcpZmBpRSSimlVM5xRefEQTMDSimllFJK5SwdDCil\nlFJKKZWjdJmQUkoppZTKOa5eQAxoZkAppZRSSqmcpZkBpZRSSimVczQz4NHMgFJKKaWUUjlKMwNK\nKaWUUirnaGbAo5kBpZRSSimlcpRmBpRSSimlVM5xRDMDoJkBpZRSSimlcpZmBpRSSimlVM7RawY8\nmhlQSimllFIqR2lmQCmllFJK5RxX58QBHQwo9V8r/laA2LQgVolL5JI67D4mY1njQnRKmOQcH75R\nDpEL6xA/pBbb1NyUh4S95wb2SxI+I+bV/2qA5Bw/UuoS/mEMqyhz/ZlEp/upfT6AVWIouiDaZozV\nDweJz/URGOlQeG4M8cOmayO4FQ2pXLda6HVDLXYvg3Gh8i9hIsckCIx02h1X7ZN5VN9XiN3XoeSW\nMnyDWn+ucaDyT8XE3g4TGB+n5E+bkAAk5gUou7A3kue9n/DRdRRdXsmG0/vibLAbYi236PvCWnwD\n2hdb6rkQqX/lIX1c/FdVYg1wM8aVur0AZ0YQa2wC/2+rkACYKCSvL8L93I99cAz/L2oAcD71k/pb\nPqbcwt4/ge+iaiQA7rcWyauLMBttrB2S+K+oQko7/jlvNhuYDhQCpwIlbZT/DJgLhIHDgF5ANXAH\nEEqXGZaua1t5hDN4g8OZwjnb8FVbmnPvYN6fvAMFg2Oc+OQ8ikdEM5Zd8MgA5k8ZRKgkySHXL6Z0\nx7oujyf+lp/os0GsUkPeRW23N3VTQyTn+vCNdMi7IIr4Gx5PzPIRfz9Awa+9OE0Kau8Lk5zrwyoy\nRM6J4t+1/cdzY2/evxPTrhtH6aBaLn70HfqOqGnzOfedewA7H7KWgyd+Qyphcc+kg5j7whCK+kc5\n956Z7HbYmk7F0lzq1QCpJ8NIqYv/8lqsfq0f35A+xu+P4Mz2Y+2Swn9pbf0+TL0YxP3ID71d/GdF\nkWKTdXtHLf57HxZcN4jIoCQHPLKY/BGJVss5CWHW2dux+oUSwv2S7H3PUvofVpVxu+q5dEikuoSI\nTBWRavH0EREjIpO7qO7J2epKv/akDtQ3VUQu7IrYuouz0iL2nyAF19QQPitG9MFw1vKJtwK4G4XC\nv1RjD3eIvxZI12MTOj5O0V3VFN1VXT8QSMz0k5zvI3xOFKvYJfpIKFv1rUqtsqh9MUDJH+ooOD1G\n9WPZ64i+48fZaNH7llp8Q13q3vRiLP1DHb1vraX3rbUU/yqKb6jrDQQcqH4wRGKRnbXe5pKLfVT/\nvZA+j66j6IoKKm8szli27qk8Ut/66PfaGvyjktQ8mu/VschP/nnV9H9jDf3fWEPR5ZUA9Hlsff22\nXn/fgH9Ust0DAXeJTeqhPIL3leO/pJrUnQUZyzrPhzHrbIJPbMTaKYXzTATAG0jkuwSf2ghGcKYH\nMUlI3VKI/zfVBB8vw1QJzpPp8vfn47ughuBTG5HtU6QeymtXrK1ZB7wLXAAcA7zURvkVwKx02WHA\ntPT2tcA44Nfp27YcCLzAsVzEXzHdvI54/fx8Zly7PWfPmcURd33JaxfvnLHsqhnFzL59GEfc9SVD\nDi7n+YmjuzweZ6VF9LkghVfXEvlRjLqHsh/L8bf8uGUWRbfXeO3N64H6x0wU6p4METk9Vr8t9koA\nsaHoL9WETopT92DH2xuAlQuK+c8NY7n2g+c56/aPePAX+7T5nM/f7s/7D+9Qf/+lO3bB9rvcteLf\n/OCPc5l6yb6diqU5d7lF6ukwwZur8J9bR+qBSNbyzmtBzAaL4H2VWNs5OC8Gve3vBnDn+vFdWIeU\nGJJTIlm3d1TFgjCf3zCI7876nD1vW84nlw3PWHbhHf2xfIYTl89l9B9XMfvnw7Nu74kMss1uPZkO\nBlRXygeGArt2tgIRGd6Rjn2uSn7iJ3BAAqvE4NvRwVQLJp6l/Md+gkcnEB8ED4uTnONNMTnLLeyh\nrXRWDYR/HMXu5xLYP4m7tmMdboD4pz5C+yWxiw3+7V3cmuwxxuf4iBzpxRiekCDxacvEZd1LAfK+\n581SJb60sfu4+Lfr2Axi7O0wke/VYvd1CYxN4FbYuNHWG+roW2HyJ1Yjfsg7tYbYdG/QlfzKj39k\nMuvr1DxQQP557Z8Nc2cEsY+MIb1drF1TmEoLk2Ey2H0viH2ql92xj4vizAikt4ewT69DLLCPq8N5\nP4g7348MdLB2SCE22N+J437uff5muQ9r1yQiYI1JYjZ2/HPebCEwFi8rMASoA1qfT/TEgO8DffA6\n/2Xp7WuB/p2OYstM4Wz+wLXd9OoNFj3bl9ETv6VgYJzB+1VSt9FPorb1zyZW6eOo+76g9861jDl7\nNZsWda4TmE1ijo/gAUmvvdnBwdRY2dubT/yEjoojPggdmiAxp+FYjj4bxD82hT24YVbcN8QlMjGK\nlQf+sUncTZ3rmnzy3FAOOPMbSgZG2XHfDVSXBYnVZl4AkYhZPHTZPow/YXn9tqGjy5l05wfkFScZ\nd8wqNq3umv3pfhTAPiSOlBqskQ6mSjCxLOU/9GMf52VH7e/GcGanB1QGfOfXYQ1wsQ+KY9ZY2bd3\n0KrnShh+xkYiA5P03reGeJmPVG3rdZWMqWP8ncsJFDsMPKaC6OpA1u2q59LBgOpK3+ANBHZN/78z\nhgOTuiie/1luuWAPaTiZWiUGd2Pmw9ndJPWdfomAqfI6v85ym+jDISovKqDm5ghupbc9cEASu4/B\n1EHsxSD+vbJ3fFvjlAu+QY1iLHJxyrLEWG7hS3cQrIi3HKjJ41VCcplFYGzKi3FXh7xjs3U3M8S1\n3sa3Y8P7sfs4ON+23tFy1tn402WtAoNb7sWfXBig8pYi1n5nAGU/7d3ifTllFsnPA4QmZDnbN2M2\n2FjbpervSy8Xs671uMwGq76s5Buo9F7fVAkyJP0593Mxq73nm8pGy6yW+KDI288yIoXzQhhTKzgv\nh7DGd3x/blYF9Gt0vwCoyFJ+J2AgkMTLKOyS3r4GmAHcDPwd2NjpiDruKU6mV/2wpPtUrwrRZ0x1\n/f2CgXGqlrc+W77DMRsZsGcVyajFjGu3Z9TJ67o8Hrfcwh7SMOi2it3s7U2j9kkiYNLHsrPGIv5G\nAHe9Rc09YVJLvTr8Y1NIus+d+sKHb/vOLRHatCrC0NHl9fdLBkQpW5E52/Wf68ey14nLGbJbw3PG\nHb2aSJF3zH/+1gB22GdDp2JpzmyysIY1vC8pdTEbMu/DxuUlD0i32fYhCax+LqYWUs+GsPdNZt3e\nUXWrAxSPblhmFh6QoHZF6535gUdVEijyYlz3VhG99q7Jur0ncpFtduvJ9JoB1ZUW4A0ERqT/j4j8\nGrgEiAK/MMa8nF7yUwTsDOwFPGyMuVRE3ks/P19E1gLPG2POT9ddJCKvNC6fKQgRuQDY1xhzdvr+\nNOBRY8yTGcpPAo5I3z0Kb9nzycYY0yh+Af7PGPNwlvdlgIeAg4Cn8QY1p+L1df6c/n8VMMkY80Hb\nu9NTc3sEZ0nTTqF/TKp+nT8AQYOpy9LYuDQt76ZPLMMcgt+LY/UyRB8JEftPkMjEhg5szc15uOst\nwidnb8wr7g6RWto0xsBop8lrShBMtqXMLliNyptmy2mj7/sJ7Z9i82/EdPq3YhzBym8UV9jFVGc4\nKbvidbYbPRfAPypB/tnV2AMcqm4povrvhRRf2dD1rXs2j/BxdR2L0QXyGr1W2EBNhgpcINJoB6Xj\nImgaXjNsoFawdktiqi0SNxYgJS7OMxH8V3vLmnwnREn8sQjnvSBUW9iXV9MejwGrmm3bEQg2uu/H\nm/1vyzRgEdSv0C8BDsQbKMwAXgXObFdUW66nnK6NIwQLGwaG/jyHWIU/yzPghYmj+fqFPpw148Mt\neu2av4RJfdOsvRmbQhqNRaTN9kZabW9irwSw+roED03grrGouTmPwptqsAq9siYJ0WnBJkuIOsJ1\nhXBBw4A2mJektqL1juzKz4v59OXBXD3jRZ67qeXSqmTc4plrxnHmLbM7HEfixjzcr5t2r+zdk94x\nWR+cd3xm5NK0fLPxUeLaAsw6C/8Z1e3a3l7GAX9hw4vZEZdERfauohMX5l87iN1vWdGu7arn0cGA\n6kpf4nXmhwEfAYcDxXirBwYAb4nI2HTZc9KPfwssEZGrjTEHicgEYLIxZkKzulsrvylDHNOAa0TE\nxuuTHEDb/YkfAKfgLXn+GhgnIr2Bs4Ax6fcxX0SeBfYFfpzhfd2PN8lZAUzF69eMBErxsh4HAfel\nn9uEiPwE+AlA+Ow7CH5nEgD5l7XsQUcfCzU9GScka09G8gymVpBgw0kXIHJ2w0k3eGiC2ruapsQL\nJtcSf9tP7f0RCn5bm7H+4otbnrxrngw2jTFJ9hgjBrdOsNMx0mxiK/aBj+JLM19E2V5S6DbJOpiY\nZMyRWgWuN6uZPimbuPe84t83dPwjJ9dS/uteTZ4XfSlC6d0dnNPOd5t2/uNkzt0WGKixIOwNCMzm\n/o+AMemBUtz7m5AABO8qx3k5hDMngAxxsPZJYFxI/rmA4L2bkP4u7jw/yRsLCVxf2Waop7ey7TWa\ndv5TtK9zfSpeGvEJ4FIaRuXgjfzfbkcd/2tCJckmnf9U1Eas7BeDnvjkPJa+3otpJ4/jwm/e6/Rg\nOb+VY6zu8SBuo2bItKe9qWvZ3jhLbMKnxAns4Q10kgt8pBbaBPb27kenBbGHuvjHdC4zkFecoLay\nofOfiPqwWtlvxsDUS/bl7Ltn4Qu0fhHv01ePY+jYcsZ899sOxxG4smVbmXwwDI2X2yQytzuANzFQ\nKxDavA+b7vDgjdWkXg+QuCtC8JqaNre3O/YSh2Sjzr8Ts9r825t/zSCKx9Qx8MjKdm3vSXr6jP22\nosuEVFf6GtgeiODNmH+IN4tfboz5In3/oHTZ540xs40xq/GWCRe2UXe7yxtj1uMNTA4CDgXeMSbr\nnDTAx8aY540xNcBXeJmLo4BHjDEVxphlxpiC9ONHZ3lfs4CaRv9aeIOY7+NdM/kIMFxEWgzEjTH3\nG2PGG2PGbx4IZGKPcEh9nV4CYsBZZmOVZP5misblnQ2CBAxujZCY3RCGW27Vn+CT83z1HUz/3knc\n1R1vKnzDHZJLGmJMLrexSjKfVPyNynsxNjyWWmN5a2d7df6bbjYL7JIg8VmwIa6FAey+rXc+/Lsk\nSMzzyqZW20jY4FYK0TcbpkmdDXaTljS5xIcETLsvHN7MGplqWMtvwCz2I71b/0ytnZK4X3hl3TVW\nfafL2j6FWeR9pu5XPqRPeqlGbxffWXUQFXyTaryOYrmFqbCQ/ukyOyVxv8o++5zNQGBl+v8Gb7lP\ntoN6Od4BAl6jEcUb/3zaqEw13q7d8k/9v0v/8VWsnuVd2G4MrJ1TQMGg1hfpr3yvmNr13sEy4ogy\nopv8JOs6f+1Ha3wjHJxvfPXxOMvbaG+GN29vvO0SNli9Gj3P700CACQ/85Gc7ScysfMD/hF7lvH1\nB33r41w2t5SSgS2b/rKVeaz4rIQ7TjmUi4eeyku378rDl+/Ni7d5l7vNe2UQHz09nEl3zep0LM1Z\n26dwFzW0h2apjZRm3ofW9g7u5mN5XcMx7szx1V+vYe+fxKyys27vqNI9atn4YX59nOVz8wgPzLzk\n6NtXi1jxdCl73bWsXdtVz6SZAdWVHLzzf+MVBKbZ/zff/yZDmUw6Wv4p4AS8byhsdXlQR+sXkZOB\nma2UqX9fxhhHvCm5xj1BAS40xvw7XU8pLZK+HeMfmyT6WIjEzinc1TaS72Jl6SgH9k1SNyWMVRgl\n/moQ/54pJGCIPhLGHlCLFBriLwbw7+E1+smP/KS+sgmfGif5kR+7gxfpAgR2S1HzRBD/KAdntYWV\nb7CzfG1lcO8U1Q8FsfINdW/4CezesEwiMd/GP2qLdlnD6xwQo/LWYqJ7xUh97ccqcrH7t153+Kg6\nKv9UglXiUPtoAaEJUSRoqLylGN+wjVilLjVTve2bxWeECOyV5erKDKy9EyT/lo+1exKzzIZCF+nb\nemfBPjRO8vYCpNgl9VQE64BEw/a/5uM/v5bUA/nYhzXM1TuzA2CBfWB6lFfoggupl0JYuyVxng9j\nDU+19nLtsgPekp4RwHq8rwstylJ+KfAxcGL6/4V4B+t76ef1B97BS6vl2tzd9kdv4M1fjmT4oWVs\nWFBAuFeSwiGtL51Z/nYv5tw7hO8/OJ/lb/WiYFCcQF7XHCub+cekqHsshG9nH85qC8k3bbc3U0NI\ngSH+WkO74tvJITHDjz00jrPKwllqYY9wcNYJtX8PkX9ZHVbnv9CKsd9dzaO/2YtdvrOGVZ8XU9Ar\nTq8hLQcDvYfW8veNj9Xff+basfQeVsPBE79h3dcF3HfugVz+7JvkFXdu3X1rrN2TJP8VwdothVlp\nQ75Bemfeh/aBCZL3RZBCQ+rFIFb6ui13VgD3Sx/+M2M4M/1YOzhZt3fUwO9WMPeKofSbUEXl52GC\nvVLkDWn9WqLqb4J8cO52HDJtEYFip83tqufSzIDqal/hzcoD7AOcKSLFIjIqff/99GOZWsGNwGAR\nsUWkJL3UJ1v5TJ4BjsPLDLzYjvKt1f8qcIaIFInIQOBuvJWcL5P5fbXmDeAsEQmIyBi8L17ZomNP\nwpB3cR3xV4KkFvqI/NTrjKYW29Tc2vLbL3yjHILHxKl7OAwhQ+iEGBKAyFlRam7Jo/p3+VgDXELH\ne53Y0A9jOMtsKi8qIPlhgMikjs/WWWEoujBK9HU/iUU2hed5dSS/tqi4s+VXoQZ2cogcmaTm8SBW\nCPK+39ChTiyyO/ytQRnjyjeU3lJG7cMFxOcEKbl+E4lPA5Rd0qtF2eCeCfLOqqbylmIk4lJwQRUS\ngqIrKij7WR82nNoP3/AUBY2+NSjxSZDA6I5fiCt5hsDkSlJPhXHnBfD/tgp3gY/E/7XsUltjk9in\n1pG8Ox8iLr4fe8sS7CNj2PsmSN5RgDUqiX1cw+eWmpKH7ycNywbED/4rqnAejZD4SSnufD++Szu3\nzhi8jvypeCmx5Xjr7qAhHdbcAXgH063Am8DJ6e3HA88Dd+IdlEd3OqL/XsFChxOf+pSPbhvOindK\nOO6Rz1g1s5gnjt2jRdl9f7MU4wh3DZzAO7/bkeMfn9fl8UgY8i+OEns1QGqhj7wLGtqb6ttaHsv+\nkQ6hoxJEHwkhIUN4c7vy/TimTqi8LJ+aWyNEfhzDyoP4mwFMnVBzR4SKn+dT8fP8Jr8t0l7hwiSX\nPDadV/6yCwvf7c9Pp77Holl9+PPxh7W7jtfvHUVdpZ/bf+BlDS4eeioVa7J/dXN7SAQCl9eQeiGE\n+7kP/6XeMesutElcn9+ivLVLCvu4GMkpXpvtO9Xb576zopglPmLnFOHOCOD/SW3W7R3lL3Q58NGv\nWfiX/qx/r4D9pn7Dhln5vHPCTi3KLr63H8lKm3dP3pFpw8Yxbdg4omv8Gbf3RHoBsUeMybUErNoa\nRGQq3oW3I/BWCwxOP1SLd6FtDLi00QXEGGMmp5+7DJhgjFmWvn8v3qy+gzfheEUb5acC040xU5vF\nNANYY4w5udn2qcAHxph70/cnpeublL4/He+6hekicgVwEd4S6N8ZYx5Ll9l8AXHj95W+5th7PjAh\n/ZLXAbcDJ+GtfLjYGPNGtv1Z8lBljz8wA/GePZfgG99z16lutqm8Z3/l3pWH9u3uENp0VQ8/yf7W\nvNLdIbTp3n/t190htOnO06e1Xagbnf/8cd0dQptO/N7i7g6hTY8G996mB/QfeXmbnWuv4ege21jp\nMiHVJTZ3pDO4pVnZyc3uD292/0Kg8Y+CtVW+xWun1+R/gTcrnzXW9CBiaqP7Exr9/ybgplbquIWW\n70uaPX96o4cvSd+UUkop1QM4PXwyYVvp2VN7SnXeWmAU8Gx3B6KUUkop1VNpZkD9TzLG9O7uGJRS\nSinVcxnNDACaGVBKKaWUUipnaWZAKaWUUkrlnJ7+LT/bimYGlFJKKaWUylGaGVBKKaWUUjnHMdsw\nM9CDkxCaGVBKKaWUUipHaWZAKaWUUkrlHL1mwKOZAaWUUkoppXKUZgaUUkoppVTO0d8Z8GhmQCml\nlFJKqRylmQGllFJKKZVzXJ0TBzQzoJRSSimlVM7SwYBSSimllFI5SpcJKaWUUkqpnOPqj44BmhlQ\nSimllFIqZ2lmQCmllFJK5RynJ0/Xb0OaGVBKKaWUUipHaWZAqR4oFnG7O4Q27fxhuLtDyCp8UkV3\nh9CmZcGC7g4hq4cXLuvuENoUH/lKd4eQ1fVyVHeH0KbH/1zT3SG0ye+mujuErFI+090htGnm4v7d\nHULbdtu2L2e25TUDPZhmBpRSSimllMpRmhlQSimllFI5x9VrBgDNDCillFJKKdWtROQBEZklIr/P\n8HiRiLwsIq+JyDQRCYiIT0RWiMj09G10Z15bMwNKKaWUUirnOD3kmgEROQmwjTH7icg/RWRHY8zi\nZsXOBG4zxrwuIvcARwGrgMeMMVdsyetrZkAppZRSSqnuMwF4Iv3/14ADmxcwxvzNGPN6+m4fYD2w\nL/A9EfkonVno1CS/DgaUUkoppVTOcZFtdhORn4jIx41uP2kUSh6wOv3/TUC/TDGLyH5AiTHmA2A2\ncLgxZm/ADxzTmf2gy4SUUkoppZTaiowx9wP3Z3i4Btj8fd35ZJisF5FS4C7gB+lNnxlj4un/fwzs\n2JnYNDOglFJKKaVyjjGyzW5t+ISGpUFjgWXNC4hIAHgS+D9jzPL05odEZKyI2MAJwLzO7AcdDCil\nlFJKKdV9ngXOEpHbgFOBz0XkumZlzgX2AH6X/uag04BrgIeAT4FZxpg3OvPiukxIKaWUUkrlnJ7y\nOwPGmCoRmQAcAdxsjFlLs1l+Y8w9wD2tPH3Mlr6+DgaUUkoppZTqRsaYchq+UWib0mVCSimllFJK\n5SjNDCillFJKqZzj9pAfHetumhlQSimllFIqR2lmQCmllFJK5RxHMwOAZgaUUkoppZTKWZoZUEop\npZRSOcf0kK8W7W6aGVBKKaWUUipHaWZAKaWUUkrlHP02IY9mBpT6L5V6NUDsvCLivynAXZf9UDYO\nJO+JEDuniMSf8zDJRvW8GCRxVT6JuyKYipYNY/yP+TgLtnzeYO0Ci4//6eezJ3zEqtr3nG/esln/\nZcN7W/Kuzcf/9NffPrjXz4aFW9aMrZ5Swvs7j2T2odsTXe5vs/yXlw5kzaPFLbZHl/uZMWYnUlVd\n06xWPZ7P8oMGs+qU/iRXZd7/xoENfyxl2QGDWfeL3piEt33VD/qz/KDB9bcluw0l9a1d/7yaFyKs\nObfvFsVY+XgBSw8cwsofDGgzxvV/6MWS/Yaw5tI+9TE6FRarzurP0gOHsOqH/Uksbaij4sECVk/s\nx7orepPa2LWnqjn3DubO/hOYMn5fKpaGs5Zd8MgAHjt8PNNOGcumxZEujaMzHuEMzuaf3R1GvaqZ\nPlZcHWb1HSGSZe3rWG14IkD17K0zF/n630dxwfDT+e0Bx7F+WX67nvO38w5m+kM71t9//7Htue6Y\no7j9jENZ83Vhl8XmvhIkNamY1OWFmHa02c5fI6QmFuPcnF/fZpsawfljAamTS0hdXIRZ6h3TZqGP\n1GklXv2TinH+mf3vurnqJ/JZOWEQa07rR3KVnbGccaBscikrDx7Ehl82tDeb1b4YYd35fervOxst\nlu85hJUTBrFywiA2XN6rQ3GpbaPHDgZEZLKIfNbo/nQRmdTBOqZ29DlZ6jpfRL4VkbUiclGzx/YR\nkYUisl5Efp3etm96W7mI/ENEMh9dnYunVEReFpGNIvKMiBR0QZ2TRGRqo/tDRGR2O59rRCSU4bH9\nReSZLY0vy2sfKSIbRKRDQ/z039hkERkuImu3Vnxbg7vcIvV0mODNVfjPrSP1QPZOivNaELPBInhf\nJdZ2Ds6LQW/7uwHcuX58F9YhJYbklKb1pN4MYL5ou4PcltoyYdXHNmNOSzLiYIel77bdEVjxoc2G\nr5o2Udsd7DD+nCTjz0myx4+ThAqhaIjb6bhqvgiy9M992eutb9jppm9ZdMXArOWX3NCXtU+0HAgA\nLMj+JsMAACAASURBVPq/AQz7xUZ8hZ2PZ7PEIj/l9xYx6Ok19P7dJjZeV5KxbNW/80mt9jHs7VUE\ndklQ+ZDXeRn89FqGvbeKYe+tYsC/1hLcJYFvoAOAWyuU3VZCryvLOx1j/Cs/m/5WxJCnv6X37zex\n4drSjGUrHy8gudrHiHdWEtwlQcWDXozl9xdReEINI95fScFxtWy80auj+vk86t4L0+faMuy+Dhuv\nz1x3R62fn8+Ma7fn7DmzOOKuL3nt4p0zll01o5jZtw/jiLu+ZMjB5Tw/cXSXxdEZL3AsF/HXHrPO\nObFGqHjDz8BfxOh1QoKyaYE2n1P+ip+aT7bOQGDFghKeuWEcN8z8D5Nu/YApl+3f5nMWTB/Au480\nDAS+mtmXl+7elbNvm8XOB67lb+ce0iWxmWU27hNh7Nsqsc+vw70/e5ttXg3Cehv7gQpkuxTmee/0\n6j4Yhu1S2A+XYx0Ux50aqa/fOiWKb2oFvqkV2OdE2x1bYpGfyvsKGfDEWkr/r5zyLMdbzRP5pFbb\nDH5zNYFdElQ93ND1cGuF8juKKflNRUPdCwPkH1fDkOmrGTJ9NX1uLWt3XNuCY2Sb3XqyHjsYSBst\nIvt1dxAiMhK4CZgA7A9cKyKNzyBXAn8yxvQF/pnu+D8BXAUMBHYDzu/isO4AvgD6AkuB/+vi+jHG\nrDTG7NV8u4icICLjOlDPTGPMSV0bXROHAr2BMVvxNXoU96MA9iFxpNRgjXQwVYKJZSn/oR/7uBji\nB/u7MZzZ6ZO2Ad/5dVgDXOyD4pg1DU2CqRScaSGscckMtbZf+RKhz0iXQB4U9DekouBkqbZuE6Ti\n0GvHzB3rjV9ZFA/16uysDS8VMuC0CoIDUhTtFSVZZuPUtt5o1y4Mkiy36XdCZct6Xi4gtjzAoB9v\n6nwwjV/rzQgFx9Xi6+sQGpfALbdx6zLE9XqEorOrkAAUnV5N7VstZwQr7i+i+IKGuMvvLiZySJTA\njp3/bGvfjFBwfA2+fg7h3eM4bcRYfHalF+MZVdS+6XVgEt/4CY2LAxDaK0ZqfXqW04U+V20iMCxF\nwfdqSC7b8gHpZoue7cvoid9SMDDO4P0qqdvoJ1Hb+lxNrNLHUfd9Qe+daxlz9mo2LerezMAUzuYP\nXNutMTRWu8BH/vgUviJDaLiLWyu48czlE2sFp07IH5faKvHMfn4YB5/5NaUD69hp3/VUbQwSq808\n8EjEbKb+cj/2Pn5p/ba6qgDn3TWDQaMqmTBxUZdlBsyHfuQ7Xpsto1KYKitrm21mBZATvDZbjo7j\nfuQdAzImhXVWFAmBjE9iyrw22yyxYYTTqdjq3gqT932vvQmOS+BUWBmP5bo3IxROqkb8UHBaNdG3\nG46Jir8WET64abuSWBggMGrLzyFq6+rpg4Eq4ILuDgL4HvCqMWaRMWYJ8ApwbKPHS4CVAMaYMmAc\n4DfG/NsYEwX+ma6jS6Rn4E8BbjDGuMCTwGFdVX87nID3HnuKw9j2+6BbmU0W1rCGhl9KXcyGzIdz\n4/KSB1R5Db19SAKrn4uphdSzIex9Gxrt5JQw9okxKN7yme54jZDXy9Tf9+dBvDpz+XCJlwXIlutZ\n85lF/zGdO/nVx/Wtn7xdG87Igf4pYitbn92MjIwz8uY1LVpNNwmLfz8AK2iY/+OhrJu25Z2H1Fqb\nwMiG/Lvd1yH1beudGmedj8BI73OzCgzupqYd29RGi/j8IJFDvZnCxFIflY8UkFrpY92vehNf0PZs\nbusx+giObPh78fVNZYyxcVm7wOCkYwzsmKTqqXzcGqHqiQIi+3ufReHxtfgHp3CqhfJ/FJH33bpO\nxdia6lUh+oxp+OMrGBinanmrSU12OGYjA/asIhm1mHHt9ow6eV2XxdEZT3Eyveg5M6tOpRAY0NA+\n2EWGVHnmg9bfz9D7pARbK7GxaXUeQ3drGJCXDqhj44rMS4WeuWEce5+wjCG7NWTIdj9qFdvtUUYi\navP0Dbuzz4nLuiQ2U2YhzdpssrXZZRYyzBs0SZ6B9PJD68AEm9cZmHl+ZCevjFnqw30gvazoqoJW\nl3xm4qyzCezUcCzbfRxSa1ofIKfW2fh38tomq8DglHtxJZf6qH7Ma1c2/KYX8c+9diXxpZ+qKYWs\nPGQQa87sR3JZz7pU1TXb7taT9fTBwJPA8SJSnyNvtoxluohMEJFlIvKoiKwRkRvSy3UmposdJiKL\n00t2xjd67m9FZIWILBeR7zfavkxEDhWRt0XkX+nN2wHLG8W1AhguIsenl5fsDzyTXkI0Mkv54eml\nRtPSZe8TESv9ujuKyHvp5S5Pi0gkvd2IyOnpWJeIyK7ADkCdMWZjuv55pAdNm5f6iMiv0vtjZHp7\nPxF5IV3/myLSN7197/S+WQR8p/HOT8e7rNH9PdLv9zTgL+n3cHgbnyHpz2h6s22tva9s+2F/Efki\n/dk+LyLh9PZiYBBwH16GoK36j01vm4eXsWkr9iPT+2eliPy20fYL0/WsFpErGr9PEbk1vXzr3UZx\nXpCuY72IXNfW6zaWuDGP2HlFTW6kgHCj1iVoIMOMNgBus/LN+tCJawtwP/VjH+Q18s6nPqiy8B3W\nbEFoOyx80ddkXf/H//RjXLADDa9v+w1OPHO8bS34ql4rBPIgtIX9buOAr6BhZ9gRN+Oa/0wxrf9P\nEckNNoPOLaPvcZUsumIgZW+2b61yRi5Y+Q37ywq7uNWtx2UcsPLcJvcbq34mn4ITa+rjr5xaiH9o\nisIfVhMcmWDNuf1wyjpxKnDBym94XQkbnEzXSzQruznGwpOrqZqWz7cX9qP27TBFpzcdIX57bn/q\n3gtT8L2ajseXgXGEYGHDzLQ/zyFWkT3z8MLE0Xx851B2v2Bll8XRGd250GDdlCArrg43uRkHrGBD\nGQmAG+v8cb2lXEcIFzZ0aoN5KWorWh/srvy8hLmvDOGkKz9t9fG/nnsIr/xtFw4/d2GH43Cuz69f\nu7/5hiMQadQGhwzUZjnuXJqWb3ZcmyrBfT6Edbw3gJYRKeyrqrGnVsBgB/eJDlwz4ArS6Pi0wiZj\ne4MDVl6juFLeh1r1r0J8Q1IUnFZDYKck687vi1Nm4RucovdtGxjyzmoiR9RRfkvmJY+q+/T0wcBG\n4AXgrHaUfQmYAfQBrsFb0gMwChiNt4xmCoCIHI03i7wzcCRwv4g0Phv8GfgDcHH6fghonPxMAGFj\nzH+MMf2BmcBJxpj+xpivMpVP/38AMBUYmo7r1PT2h4Hr0483z4gcjTfAeBn4CVAM1J8djTFRY0zj\nFu1IYASwB/B1etudwPNAP+B94Hfp7VPT+2YMsAtZGGPmpN/vv4FL0+/3jWzPaUPz9wWZ98OVwN/S\n8c8Ddk9vn5B+PzOBfUWk8bRDk/rTHfMpwIl4A58WS6AaE5FewIN4n9FuwGkickw6M3MWsC+wI/Ar\nEdnc+9sPb9nWAKAQOCq9/ZZ0PEOBkdLKNR4i8hMR+VhEPk69PrV+e+DKWkL/qGxyo6DZiSQh2Y/m\nvKaDBZNselYO3liN74yodxFxHFIPhvFd0LnZ2FHHpurX9W+++UKQatT5d1OyRT2bDV9Z9M6yhKi9\n/MUOqcqGGTA31vG4qj4JM/CscgaeWUH/UysZfF4ZG1/Zskt4rEK3ScfajQtYrU8t2UVNBwqm2SCr\n5vk88o+prb8f/yxI6WXl5B0epfj8KgI7J4jObn1mvK0Ym7xuTJAMf4MtyqZjXP/73gy4Zz2DH17L\nwAfWseaSPk2eN+SJNfT6ZTnrrmy6fUuESpJNOv+pqI1k2LebnfjkPE5+bi7TTh6H6eEzfFtLv7Pj\nDL0q2uRmRwxuo6UuJkm3jljyiuPUNer8J6I2ViufrTHwj5/vz3l3zsAXaL0duezRt/j1U69z+xmH\ndvgzt39bU792f/ONArfphE1cQLJUnN9sgifRdMe690eQQ+P12Qb7Z3VIPxexwDoqhvms/UvrrEIX\nt6rp8Zlp4NbiWE7PF8XnByj5RQWRw6IUnVdFYFSC2MdBSi6rJLiLN0ArOK2G2EfB1qrtNsbINrv1\nZD19MABwL5mXCjXeu7PwOsib/9383h42xsSMMdOA7USkEDgcryP4DfAOkIe3tn+zm40x7xtjNk9T\n1eF18DcLprdlkq38pvQgIgE8g9eBLUjHMwVYBXwfbxCz2TXGmBTwMVAEJNN1ejtBZICIXNOofAVw\niTFmjTH184SH4w2SvgV+htchLQKGGmOmGWNiwCNZ3tPW0OR9tbEf3gfOBS4FHjTGzExvPwz4Ll4H\nvBDYO1P9wEhggzHmE2PMJmBaG/HtD3xqjPnMGFOJN3A6Jr2vJuINCJ4ASvGuWQBYB/zVGJPEG7QU\nNYr/T8DpwE8b/W3VM8bcb4wZb4wZ7ztiUtbArO1TuIvS66sNmKW2l3bOWN7BXeSNk9x1FhL0TkLO\nHB8mPWy1909iVtmYJTZmo0XidwXEzivCnRkg+ec8nBmdX7ed39dQvU7q463dIATyOterMgbKl1kU\nD9/ywUDBuCiVsyP19VZ/FiY4sGPrmX0FLsHBDbORVtDgK9qy5UvB3RLE5wXq40p8EcTXt/U6A7sm\niH3qNQfJVT4k1LBfE9/4kICpv3AYvCyCb0DDe5SAwerERc/B3eLE5gbrY4x/EcDu1/q+a1w2ucqH\nlc5SxeYFCezg9SYCw1Kk1vpwY0LtO+H6Geb8o+pIfN111wz0H1/F6lnF9XGvnVNAwaDWF7qvfK+Y\n2vXe5zDiiDKim/wk67r0uyD+qwWGuMSWNbRDiVUWvqLuGy1tt8dGFn3Ytz6epZ/2pmRgy1N12co8\nVswv5c+nHc4Fw0/nhTtG869f7cvzt+/Gl+/3o3K9d/oec9i31JQHiddt+dIW2cHBfOWrj80ssaE0\n876SHVIN5ddZXvY3zX01iFlrY/3QW/pnqgV3VsMxYsqsjJMHrQnsmiAxr9Gx/GUAu1/m9mZz25Rc\nZde3N1Z+K+1KvqHmuYaLupz1Nljk7IC6J+tZi7daYYyZISIO3kzw1GYPD2r0f6fZv/VVNPu/hTeI\n+JMx5iaoX2rSOA/9QbM6luDNAm82DPgoS9hLgOHNym++QqnxAMzCSwYKEEvPuiMiAZoOJr5p9l6+\nAXqLSL4xpgbYHm8G+o/pxz9KX0vQmABjjDHrxLvAuYCWg8Et7111TPP3lXE/GGNuFpHX8Dr/b4rI\nOcaY19P3DzTGzBeR6/GWCs3MUn/jv4f2vN/mfz9GRLYH3gUmA5fTdLnRUmPqm7rGzz0OOAQ4ArhB\nREYbYza04/VbZe2eJPmvCNZuKcxKG/IN0jtzC2sfmCB5XwQpNKReDGLt5XVe3VkB3C99+M+M4cz0\nY+3gYO3sEHqw4WLTxN0R7AkJ7N06f9Ff8TCXZTP8FA1yqdsk+EKGYCcnz2s3Cv6wwd/xyewWeh1W\nw+LfD6DkoFpqFwbxlziEBnXsYrfi/WpZenNfBk3chJuwWPtkMTtcu2VfThU5KErZTSWE94mRWBzA\nKnbwDWj95Jx/bC0bJ/fCLnWofLCQvMMaOj9174UJ79P0KsXQ+DjVz+UT2LmcxCI/8QUBgrt2fDlY\n3sFRNt5YSnjfGInFfuxiF3+GGAuOrWX9VV6MFY1i9A9OUflgIflH11L7VgQr38UKGWpejRD9OEjv\nyyuoeSVCaEyWq1I7aPujN/DmL0cy/NAyNiwoINwrSeGQ1q/kXP52L+bcO4TvPzif5W/1omBQnEDe\nlg30/pdERjlsei5A7Y42iTUWVp7BV9x9Pb1x313FQ1fsw24TvmXlFyUU9IrRe0hti3K9h9YyZf1D\n9fefvG53+gyrYcJZi3n6+nG88Y9RXPTAuyyYPoDSgXWE8rb8gmfZI4H7zwju6CSssKHAIH2yTOAc\nlMC5Jw8prMV9PoTs4x2j5ksf7qNh7JurqF/PEDC4/4ggA2ug2MVMCyN7t78dCx8YpfyWYkL7pI/l\nIgdf/9b/zvOOrqPsmlLsEpeqhwvqr0UK7hmn5rk8SkZVkFzkJ/F5gMBucdb+qD++ASkCIxNU3ldE\neEJ0qy8X6wj9nQHPf0NmAOAevFnfUmAIgIgcg9cJbsvpIhJMXxew2hhTAbwBnCoihSIyEK/T2Pr3\nBXpeAI4UkZEish3eMpwXs5SfB0TTa9bDwDl4S3QAikXkRBEJAicBM40xVcACETkzXeYG4OrNlTXq\nXG6+vxF4G295igVMSt/P5g3gvPT/fwZMMcaUA2tE5PvpeE5vo47NNuINcBCR3m2UzaiV95VxP4jI\nq0CeMeZW4C1gvIj0x/t7+Dxd/kMaXUTcvH5gETBARMalB4AntBHiTGCciOyWzij9GG/J0e7AMrwL\nw0cCgxu/jeaVpK97WADMwRuw1eBd99FpEoHA5TWkXgjhfu7Df6l3wnMX2iSub7le3dolhX1cjOSU\nMIQMvlO9Btx3VhSzxEfsnCLcGQH8P2l54uwKvgCMPCrFmnk2VastdjzCO7lWrxG+fKFjcxJVq4X8\n/l3T4fAVuoyeuoKV9/SiYkYeu9y3ksqPwsz74bB211H6nVp6H1PNhwfuyKy9dqT3kdX0+s6WrXG3\n8g397thAxb8Kic4O0vfmjcTmBllzYcvlMuHxcYrOrqLs+lKsPJeSixoGcrHZIYJjm3b0iy+oxK2y\nWDFhEGsv6EvvqzZhF3V8HsDKN/T/ywYqphYSnR2i3y0biM4J8u0FLX+7ILxXnJJzqthwfS+sPEPp\nRd5XD/a9biM1r+Sx8sSBVD2TT7+bvMugev+6nPgXQZbuP4Sal/LoM7nrLpoNFjqc+NSnfHTbcFa8\nU8Jxj3zGqpnFPHHsHi3K7vubpRhHuGvgBN753Y4c//i8Lovjf4EVgr4T41S+4ye2xKbPGd6gLbbM\nYu0/tv1ykEhhkssefYsX79yNL94bwMVT3uGrWX256cQj213H9385H9ex+Ol2p/Pvq8bz8wfbOrW2\nj0TA/k0N7nMhzAI/9i+9NsIs9OFc27LNll1TWMdHcf4RgbCpzwK4z4YgKjhXFHrXI5xbjATB+kkd\nztUFOJcWwSAH65T2f7WolW/oc+tGqh4sIP5JiN43lhH7NMD6n7Vsb0Lj4xROqmLTTSVYeYain3rt\nTdH5VbjVFqsPG8j6n/Wh9A+bsIsMva4to2xyKauPGYhxoPS3nf86Y7X1SMv+Us8gIpOBkDHmyvTy\nkW+BK4Az8C6fXACMxVvbPxVv7fhkYHq6ignpf0vx1sMngEmbl5eIyB/wvu7TAX5njHk0vX0ZMMEY\ns6xZPOcB1+INoCYbY+5p9Nj09LbpjbbtA/wLb437U8CFeB3XGenbwXjLVH5mjDHpC30fwBvgzAF+\nZIwpFxFj0ovNxPvNhAnGmEnpC4CnAnsC7wHnGGOqGpdpFn9/vM7r7njXEfzIGLNcRA7EW5bjB2YD\ntZufKyLDgenGmOHN6hoBPI23/v1xY8zFImKAxj3JL4wxe6fLT0jvnwmN6sj0vjLth+/gXTPQC69T\nfwpeFuA8Y8x3Gr3HZXjf7lSXof6TgL/gLdtajLeEaCpeNqdxPvllY8wpInIk3vUWecA9xpjr0wOJ\nF/EGAq/hLW26CO9vrP59inex+3RjzFQR+Tnwm/R+fikdd8YpxvDT5T3zwGxk97e34Ds9t4HwNYu6\nO4Q2LVu3xT8PslW1tt66pzll5FfdHUJW18tRbRfqZtv9uesu0N5abrjo8e4OIasfvXpid4fQpkHb\n9/zPedluQ7fpVP0JNXO2WSP3bP4ePTYN0WMHA/+LMnWulWpOBwNbTgcDW04HA1tOBwNdQwcDW04H\nAy0dVzN3mzVyz+Xv3mMHA/8ty4SUUkoppZRSXazHX0D8vyS99Gh4N4ehlFJKKZXzHL2AGNDMgFJK\nKaWUUjlLMwNKKaWUUirn9PQfA9tWNDOglFJKKaVUjtLMgFJKKaWUyjmuq5kB0MyAUkoppZRSOUsz\nA0oppZRSKufotwl5NDOglFJKKaVUjtLMgFJKKaWUyjmuZgYAzQwopZRSSimVszQzoJRSSimlco7+\nzoBHMwNKKaWUUkrlKM0MKKWUUkqpnKPXDHg0M6CUUkoppVSO0sGAUkoppZRSOUqXCSmllFJKqZzj\nmu6OoGfQzIBSSimllFI5SjMDSimllFIq5ziuXkAMIMZojkSpnuZ3vNLjD8y1qYLuDiGrl/8+trtD\naNOEc77o7hCyevKdEd0dQpsK1gS7O4Ssijfa3R1Cm5b8Kr+7Q2hTnwcquzuErM4954PuDqFN980a\n3d0htKl8vwHbtHd+wLovt9m5dka/nXvsyEMzA0oppZRSKufoj4559JoBpZRSSimlcpRmBpRSSiml\nVM7RHx3zaGZAKaWUUkqpHKWZAaWUUkoplXP024Q8mhlQSimllFIqR2lmQCmllFJK5Ry9ZsCjmQGl\nlFJKKaVylGYGlFJKKaVUzjFud0fQM2hmQCmllFJKqRylmQGllFJKKZVz9JoBj2YGlFJKKaWUylE6\nGFBKKaWUUipH6TIhpZRSSimVc1z90TFAMwNKKaWUUkrlLM0MKKWUUkqpnOPoBcSAZgaUUkoppZTK\nWZoZUEoppZRSOcfoNQOAZgaUUkoppZTKWZoZUOq/2Jx7h/De5B0oGBzjpCc/pXhENGPZBY8M4LMp\ngwiVpJhw/SJKd6yrf+zF83ZlyIEVjJm0ustjXHhff+ZeO4TIoASHPr6QghHxVss5CeGdH+/EyudL\nCfdPcMB9XzPosErq1vp5cofxBIpTAPQ7qIpDH/uqS2KrfddHzUsB7GJD8XkxfL1NxrLGhcrHgsTn\n+Qjs6FA8KYb4wa2F8vvDpNYJdi9D8cQYvn5ePbVv+YnNs7FLDQUnJrALM9efzeK/92HBdYOIDEpy\nwCOLyR+RaLWckxBmnb0dq18oIdwvyd73LKX/YVUZt28J94UQ7sN50NvB/n0V0t/NWNY44N6Zj5kV\nRMYksX5ThQTAxMC9sRDzhR85KI59SY1Xfq2F86dC+MYHO6S8+vu6uG8Hce/Mh6BXr3VqHeyX+XU3\ni7/lJ/psEKvUkHdRHXaf7J9z3dQQybk+fCMd8i6IIv6GxxOzfMTfD1Dwa+/4MSmovS9Mcq4Pq8gQ\nOSeKf1enHXuwfapm+qh43Y9dZOh7Vhx/r7b/hjY8ESA0wqVgr1SXxdERj3AGb3A4Uzhnm75udLqf\nuv8EsUsMBT9t+3OueTBEYq4P/0iHgvObfs6xD3zEZwQoutz7nN1KYdNv8pGIV6d/J4fCn2Zub9tj\n7r2DmTF5ewoGxzj+yc+ytt+fP9Kf+VMGESpJcvD1Xzdpv7tCfFqE2AP5WH0dItdVYA9s/W/YOBC9\nuYjke0F8uyeIXFWBBNJ1/DtC8r0Q0s8hfFE1Vql3bBoXan9ZSnBiDf49Wm+7uovbuSb5f45mBrYS\nEZksIp81uj9dRCZ1sI6pHX1OlrrOF5FvRWStiFzU7LF9RGShiKwXkV+LyP7pcpUiEk3//3eNyp8m\nIn/pirhaifM6EVkuIotF5KRG208QkRUi8qWI7JHeNllEakSkQkTeFpHdujiWySJyY1fW2ZXWz8/n\n/Wu355w5Mznyri959eKdM5ZdOaOYj24fzpF3fcmQgzfx/MQx9Y+988cdWPDQoK0S46b5ET69fjDH\nfzSP/e5YwqxLt8tYdsHtA7F8hh+u+ojdr1rBrIu39+qYl8cOZ63n9FWzOX3V7C4bCCRXW9S8HKD3\nlVEKT41T9e9g1vJ17/lxyoS+19fiH+JQ+7bXc6h5NUB4nyT9bqwjvHeSqqe9eqIf+Yh/YVN0Zhyr\nyFD1VKBTcVYsCPP5DYP47qzP2fO25Xxy2fCMZRfe0R/LZzhx+VxG/3EVs38+POv2zjJLbdxHI9h/\nLcf+WQ3uX/Ozl38xBOts7EfKkB2SmGfDALgP5UG+wX68DFxw3/H2nXtnAdahcexnNiLbpXAfiXj1\nLPFh/bwG3+Nl+B4vwzqp7c6Ys9Ii+lyQwqtrifwoRt1Doazl42/5ccssim6vwR7uEH+94XMzUah7\nMkTk9Fj9ttgrAcSGor9UEzopTt2D2evviMQaoeINPwN/EaPXCQnKprX9N1T+ip+aT7pvnu8FjuUi\n/oph2y6/SK2yiD4fpPiPteSdEaPmkeyfQ2y69zmX3lqDb5hD9I2GfetGoe7pEHmnNXzOqRUWoQOS\n9Lq9hl6312zxQGDD/HxmXrsdP57zAYfftZDXLx6VseyqGcV8fPswDr9rIUMOLufFiV16qsP5xkds\nSj4FUzYSvqyK6K2FGcsmno3grrUp/M967JFJ4k/kedtfDZH8MEj4ykqsXi7RO7w6TAqiNxSRmtu5\n9k9tGzoY2LpGi8h+3R2EiIwEbgImAPsD14pI457jlcCfjDF9gX8aY2YaY/oDlwL/Mcb0N8b8aXNh\nY8y/jTGXboU4v5eObxRwJHC3iAwXkb7AP4Cjge8B9zd62t1AH+At4CURyd6j28rS8U7aFq+16Nl+\njJ74LQUD4wzer4LoxgCJWrvVsvFKH0ff9zm9d65l7Nmr2bTI61xt+CKP2CY/O5+2ZqvEuOK5Unb4\n0QYiAxP03a+a2EY/ydrWm53SMXXsf/c3BIsdhh5bTu0q7+RR9mkepWNruzy22Dyb8D4p7GLz/+yd\nd3wcxfXAv2+v36nLvdsUG4JtigkYCJgSOiEEQgIEgoFASCCBOCE9EAyB/OgYCCUEEwglBFMMhI5p\nxkAwBgwYg3s3ktWv387vj11JJ+nuJMuy5UTv+/nsR3dzs7NvZ2Zn35v3ZoR/jI3dJNi5nRZO/gUe\nig5LIV4IH5Qi/qGjcKXXWfhGO7No/p1sMrWOEmQMlHw3gXeAIbR3mvSG7g23q58sZ9SpVYSHpOi3\nbyOJai/pPHVYPiHKpJtX4C/LMOToWmJr/AXTu4uZG0AOSyCVNrJrGlNnYeIF8r8ZQE6KIj6Q4+LY\nbwVa0q3vRBELrONimLl+jAE5PI51QgwJgkxKQpXbr5d4kR02b7Y7Od9LYP8UVrnBu2MG02hhu13U\ncQAAIABJREFUCrRz6j0fwSMTiBeChyRJzm9VrGOPB/BNTOMZ1uqN8A63CZ8Rw4qAb2IKe1PPvVab\nFnopmpTGW2oIjuq8jybXC5moULR773gEAO5hKr9n+ja/bvJ9L4H9UnjKDb4utHPyPR+hI9x2PjhJ\nckFrO0efCOCfkMab1c7plR48w3vO47P48QHs5o7fQyfXdTp+H3HHJ/TbpYnxU9e2jN89Req1IP6j\nYlj9bbzjU87zHMttzKXmBAmc0oj4IPCtKKnXXaPLFkI/r8czLIP/iBj2Knd8/E8Aa1gaz27bl0eg\nmYwt2+zYnlFjYOtSD5zX20LgKNDPGWMWG2OWAs8Cx2T9Xg6sAjDGVPeCfM18B7jbGBMzxiwD3gMm\nAUcALxhjPjbGLAFWisjOzScZY1LGmOlAGji4NwTPYhRw5ra4UP3qIAMmNLR8LxqSoG5F7tmwHY+u\nYvBe9aRiFm9M34GxJ20AoN8uTRxxy6fIVhoJmlYHKB/fqsiHhyRpXJHbXht+VA3+Uudlu+alUvrv\n44SMVC8o4qPrh/LAkL2Zvf8E6hb3zMyrXWvhG9r6srdKDZlN+QfsTI2F181vhcBudPJ6h9jE5vqw\n4xB9w0tgnHMP4X3SePsZ7Bg0Pu8jtEf3FLToGj9l41tDAkKDkzStzK3MDzmyrqUON7xcSuVXGwum\ndxdTZSGjW+9HKm3YkFuRac3vXF8iBurceq4TGOYqWANtzFoPImBNadXizPt+ZJeU8/kLL5nLS0h/\nu5LMlcUFDZBm7BqrjRJnldnYVfk7vF0jeIY77SxhMA2OrJl1FokX/dgbLRr/EiK9zCnDNzGNuLpZ\n+hMv3h16TmHM1An+wa191FNqSNfk76O+gYZ+30qyjSfl2/AvTqKSbf8asWssvMPatnOmunA7Nyv7\nVhhst53T6y3iL/vJbLSovyNEarlTRnqlh9izAaovKqL2yjCZ9Vs2aDasDtB/QutzWDQkTn2e8XuH\no6sYtFcDqZjF3OljWsbvnsLeaOHZIdXy3epnY6/P/TzbX3rw7Og8+1JkMLVOPfiPiuEZksE0CvH7\nivAd7HhOvPskCH6/5ydzlJ5FjYGtyyPA8SJS3pwgIjOzPs8RkSkislxEHhCRdSJylRuuc4ab7VA3\nZGaRiEzKOvc3btjMChE5Lit9uYgc4obN3OsmjwFWZMm1EhglIseLyHqc2fhZbjjQ2M5uSkTObHcf\nM0XkKRFZIyLXiMgyEbnT/e1oV/YNInJZ1jnnicgq916vyCq+n/u7B9gFqAKGAR9m5VkB7JhDtA9w\nvAqIyB9deVaKyOlZ17wnS4bHROTbWfnXum3wo07u3xKRO938X4jI4W7668AsoDnM6q6sc3K215Zg\nMhAoaVXIfJEMiVpfgTNg9hkT+M/NI9nzvFWuXD0hSX5MRvCXtL6gfeEMydrCIQyZhPD+5SOY8IvV\nABSPjnPIQ4s4de27jDqxind+MXqz5dh0e5ANvwq3OUwGJNgaMCp+g4kWqBDTNj/ubYX3TxGd56Pm\nthCJj7yED0q1OW3TjBCJT7wEuxm/bTLgy6pDT9juUh1+NH0ou/x8XZfSC5Z1aQnp71a2OUgD4ay6\nCBpoLFB3GYGI3fa7e15LHwwaaGpbhlntwbweQI6NYeIg+yfwXFeL54FqyAhmdqhN/sabQtT+pKjN\nQQYkS8eSQCftbAsSMm2+gxMOZA2wCRySxDs8Q+P/RbDrW8sxKYg9FiB4dIHp6AJsuCfAyj+G2hwm\nA1aW7Sx+sOP5Zd/az3NX2BYi1M8IUX1xUZvDpDevnY1p185un4w958czwCZ4sNPO9dc67ezpb1Py\noyiVNzbi3ytNYydhhZ1hMtJh/I538lw/fcZuvHfzCHY/b/UWXbsDtjhGejNBg8n3PGdom7ed7dt4\nUQXptwP4v+5Y6ttDnyyEbcs2O7ZndAHx1qUKeAo4vQt5nwH8OCEvl+OE9ICj3I7HCZG5Byf06Cjg\nUBxleRgwR0RGGGOatZBrgZ/gKMcAQVeWZpJAyBjzBPCEiMwBLjPGzNn8W2xhPXAVcCFwAo5x0R+Y\ngTNbXwPME5EnjDHvA9fgGCFfAPeJSDHwoPu5CPgaEAbeAA5wz2+mCSjLIUMjUCQiI9zzdwZKgfnA\nfcBjwOWuoeED9gdOE5EK4NfAQBwD+U7gtgL3ujdQAYzA8VzcDDxvjPmaiEzBqcspzZm70F7N+c4F\nzgU46o4L2ePcowuIAMHydJuXRzpmIVbh1VDfemQBy16oZNZJu3P+kte2+kDtL0+3UVzT8c5lnH/Z\nCComNjHsiFoA9v5Tqx27yw/XM/+yEZstR8UPO04h18/yt3WFp6Tg9IgVMk7+gCO/cd/jdfcHqTg/\nhn+MTXqjUHNHkP6/aY0n7ndJjOgbXur+HqTy4s2PM/aXZ0hl1WGmC3X40eVDKZsQZcjhdV1KL4Tn\njx0XGmfuirRV3BOF645iGxotCLoGQXPEgDjhVCJuGVlFmgxkrivG+n4TUubcr+enrTOpcmQc82QQ\nDmhVvot+2rF+ow8FsLPWWpqkFNRYJeIokdLczu5TmlnqIfTtBP49nYZPLfSSXuTB/1Xne+yxAJ4R\nNr4J3fMMDJza0YjYNNvxOLXInqJXZ/23F0ou7NjOTf8MYGcld9bOVthgRwWP28647Zxe6iF8YoLA\nHq3tnPrMQ+Sk1vYJHZwk+kTxFt1Dx/Hb06mH9puPfMiyFyp4/KSJnLfkjR4bv6XYbqv8J8lbd815\nmw0pk2ybsfiv1SSeCNF0RSnFt27qGQGVrY56BrY+t5M/VCj7KXoLR5lt/tvcNvcbY+LGmMeAMSJS\nAhyGo5AuAV4FIsCQrLL+zxjzhjGmOYYkimMQNBNw03qSZrnfAWpd+fcFhrppn7syfsXN/wZwJXAK\ncL4xpsEY8wxwElDi3t91xpg0jiGQrfyHgFxbiESARmPMSuAiYBrwDxwlH2PMRuBTHEPhEOBVY0wU\nqAM+A24Avg58v9CNGmPeBq7HMdpmAAM6qZvO2qu53DuNMZOMMZM6MwQABk+qY81bZe65sH5+CcVD\nc89Krny9nKaNTmjJ6K9XE9vkIxXNH9bRU/Tbq5GN84pbZKx+v4jw0Pyxo6ueLWPZo/3Y75YlgLM7\nzuf39W/5vWmNs1jT9MAOEL4RNkk31MMYSK2y8JTmL9g30ia51A0ZqJKWHTSSyy28biiHd4DBrrUw\nSYgv9GDcWw3umSa9rnvDbcWeTVS9XdQiZ837EUJDUnnzr32ulJWPVrD3jOVdSu8OslMa84mvRSbz\nhRcq8+/qIzulMZ86io9Zb7XsBiSjM/C5m77Yi/RrLcO+N4KU2ljHONqw2WBhv5fl+aqyuqQYe0dn\nyCzxtsiaWeHBKs8vq2dUhvQXzrOR+bK1nSVksLLv0UfLzjKpD72k3vURPmPLFpW2xz/cJr7c0yJ7\ncrWFt0Af7ct4R2VIZ7VzemXhdnbyt7ZzS58MGTwV7do5ZIjPbe17dq2FyJaNQ4Mm1bE2a/zeML+E\n4qG5495WvV6WNX5vIt7D47dnXIr0Qn+LLJnPfFj9cxu1nl1SpD9y8mbWelqM5tTcQMsaDd8hcexl\n/x1zzcbINju2Z9QY2MoYY97EcaTtkePn7C1cMu3+thTR7rOF8wq80l3YOwhnhjp7T8h57cpYihPL\n3sxIYFlX5N8McskvwCtZcg4DHnV/+wbOjPpYYKHrRcAY8wpwPxAH/uLm/Q+QvRB7EpDLTzoe+FhE\nvobjBVhKx/j9fwHfdK//iHvNDI6y/i/gIOB9Ecm7wlJETgNuBd4HLsiXL/sUCrdXt9jhqC/57LGB\nLJo1kDem70CoMkXJ8NwvkxWvVPDixeOwM7DshUqKh8bxR3outjkfw46sYcUTFSx/rIIFVw4nUJGm\naHhuY6D+iyCvT92Zg+//jECZI5vHb/jwz8NY92oJiVoPC64czohjN/XIjFjgK2niC7zE3vfQ+G8f\nVsTgqcj/dg9OStEwO0BisUX9owGCE5yZQ0+loekVH+kNQuPzPiRoED/E3/fS8IzTjeLzvfhGdq++\nhxxRy+ony1n1eDkf/2kIgco0kTx12LAkwLyzx7D/fUvwl2U6Te8usncSM9eP/YYf848wlNjIgPyK\nl3VwAvveCOZDH/YdRch+jtYgB8fJ3F6EWejFnhlpSbfnBDCvBrCmNbQpx55RjNloYTZa2E+GkMmd\nL0r0TUiTfM9L8j9e4k/6kSKDVWB7Tv++KWKzAqQWeYg9GMS3p2N4eXfOkHzT5yiaqywyyyw8ozNk\nNghNdwWJnB/FinQqzmYRHpchutBD00ceal9w+qi3TI2BXPjGp0nO95J4z0tstr/T5znw1RTRxwOk\nPvPQ9HCQwO5OO/t2zhB/q7Wd08stvKMzxJ72k1zkwY5CdLYf/+7pLRqHxhxVxeLHBvDZrAHMnT6G\nUGWSkuF5JnNeqeDli8e643cFRUMTPTp++yYnSL0aJDknSOKeIscIH5j7efYfFid+VzHp9/3Eby7B\nd6Dzzkm9EiT+N2fiJ/VyEM8u+ScslO0PNQa2DX/Bme2uAIaDE0sP7NCFc08RkYAbZ77GGFMLvAic\nLCIlIjIEZ8Y5V9hMM08Bh4vIWBEZg7NTz9Pdv50uMw/Yw72uH3jBlSMMLMQJ3/kDjkchew3Ar4Er\n3Fl7cDwLO4nIOW4ozY5uGgAi4hWRX+Eo3XOAfdzfHwTaT7HPwjEEDsGtA3cx8kvu8UtgEFBZ4L4m\nA88BTwDHt/utChgmIh4RKXdDkja3vbpEoCTDt/61gHeuH8XKVys4/h8fsnpuGQ8fs2dHgS9Zhp0R\nbh5yMHN+uxPffOiDHCX2PP6SDIc89BkLbxzK+tdKmPL3xWyYW8zz3+i4DeqnfxlMss7Di9/ahQeH\n7c2Dw/Ymus7HAXd+wZs/3JF/jd0LkxEm37S0R2SzQlD+gzhNL/tJfu6hbKrzUksusdh0a8eFfIGd\nbIoOS1L/rwBWwFB0jKOIlp0WJz7fy5d/ChN9y0fZ951ySk5IkFplseGSMLH3vJSe0r1Ycl+JzQEP\nfMGimwax8fViJs9cwpdvFfHqN3fukPfz2weSqvPw2kk78djI3Xls5O7E1vnypncXiRg8v6vHnhXG\nfOTD80tHaTcfe8n8vuO2hDI+hXVSjMxfiiBksL7nPNrWYQmsrybIzChGxqaQY526s/8ZglqLzA8q\nnHUKF5UhA22sb0fJ/KiczMVlyH4J5MjOVxBLCIouiBF/zk96kZfIec7sffpzDw3Xhzrk943NEDwy\nSewfQSRoCB3vtFvwuAQmKtRdXETjdWHC349jRSDxkh8TFRpvDLesU7Bre2YW0ArCgDMS1L3qI77U\nQ/9THVniyy3W/7VXN07b7rBCUHx+jNjzflKfeSk612nn1Bce6m7M3c6hI5I0Pui28zecug0f47Rz\nzbQi6m8MU3S6085FU+M03huk5ldFYAuRU7fMCxQoyfDNf33Au9ePZNWr5Rz7j49YPbeUR47pOG+4\nzyXLsTNw65CDeP23O3H8Qx/mKLH7SMQQubKGxEMR0u/7CV9aS/ojH43Tyjvk9e6RJHBqI9EbSyBs\nEzzLCd0L/riezGc+6o4eQOrFEOFLuh6K2JvY9rY7tmfE9IS/XemAu1g2aIz5lRsPvxZH0TwVZ/nd\nQmAi8HtgJs4agctwlFloXTNQAUzAieI70xgz1y3/98APcGbif2uMecBNXw5MMcYsbyfPOcB0HAPw\nMmPMX7J+m0OONQPuFplHGmO+myN9ijHmTPf7zHZyXwbMMcaMcrcLvQ4oBh40xkxzz/kJcAlO7P4z\nwDnGmIw4/yvgUWC37Jh6ERmH40koAS4yxsxz6/jnONGe84ALjDFLRGQUMBsnfOefON6BvYwxi92y\n3gTWGWNOyir/alq9CPcYY37tlv8bWiOcAb6H45X4J45j+UHgfGCIMabOLet2HO9DBtjRGBPL1175\n+C3PbvcP5vr0lsXMbm3+fdfE3hahU6ac9Ulvi1CQR17d/MXa25riddu3UlxWtfXD8baUpT8v/H8i\ntgf63719K5dnn9XeIb/9ccdb43tbhE6pmTx4m8bTjFq4cpu9a5fvNmK7jRVSY0DpU4iIF8dT86Ix\n5uHelicfagxsOWoMbDlqDGw5agz0DGoMbDlqDHREjQGH/44VHorSc6zHWUTclVh/RVEURVH+R9ne\nt/zcVqgxoPQpjDH9elsGRVEURVGU7QU1BhRFURRFUZQ+R0Y9A4DuJqQoiqIoiqIofRb1DCiKoiiK\noih9Dl0z4KCeAUVRFEVRFEXpo6hnQFEURVEURelzmO38n4FtK9QzoCiKoiiKoih9FPUMKIqiKIqi\nKH2OjNE1A6CeAUVRFEVRFEXps6hnQFEURVEURelz6G5CDuoZUBRFURRFUZQ+inoGFEVRFEVRlD6H\nrbsJAeoZUBRFURRFUZQ+i3oGFEVRFEVRlD6H0TUDgHoGFEVRFEVRFKXPosaAoiiKoiiKovRRNExI\nURRFURRF6XPo1qIOagwoynbI1c9O6m0ROqXf6kBvi1AQe1J9b4vQKS98OKy3RSjIUfut7m0ROuVk\n/4e9LUJBfHa6t0XolP7ldb0tQqd8eXZpb4tQEGtAVW+L0Cl77VHd2yJ0gcG9LUCfRI0BRVEURVEU\npc+R0a1FAV0zoCiKoiiKoih9FvUMKIqiKIqiKH0OXTPgoJ4BRVEURVEURemjqDGgKIqiKIqi9DlM\nRrbZ0RkicreIvCUiv9ucPF05rzPUGFAURVEURVGUXkJEvgV4jDGTgTEislNX8nTlvK6gawYURVEU\nRVGUPse23E1IRM4Fzs1KutMYc6f7eQrwT/fz88ABwOftisiVZ48unNcpagwoiqIoiqIoylbEVfzv\nzPNzBFjjft4E7NnFPF05r1PUGFAURVEURVH6HNvRbkKNQMj9XETuMP5cebpyXqfomgFFURRFURRF\n6T3ewwnxAZgILO9inq6c1ynqGVAURVEURVH6HPb28x+IHwdeF5EhwFHAd0XkCmPM7wrk2RcwOdI2\nG/UMKIqiKIqiKEovYYypx1kgPA842BjzQTtDIFeeulxp3bm+egYURVEURVGUPodsP2sGMMbU0Loz\nUJfzdOW8zlDPgKIoiqIoiqL0UdQYUBRFURRFUZQ+ioYJKYqiKIqiKH0OT2b7CRPqTdQzoCiKoiiK\noih9FPUMKIqiKIqiKH0Oa/vZWrRXUc+AoiiKoiiKovRR1BhQlP8izNNB7FMrsH9Sillf+PE1GbBv\nKsL+TgX2lcWYpJseB/vyYuzvVmDfEmnNe2pF2+PoSue3OX7sb7Wmm8eCXZY3NsdH1bQIm64Ik/my\ncGymsaH+7wG+vDhC3e1BTMpJ3zQ9TNW0SMux8dwiMtWC3QQ114fY+MMiqv8QJrWq68NZ7NEw1UcO\npPaMfmTWePLLlIGGK0upPnwg9b8qb6nDbGp/WEnyPT8Adr1Q9+NKqvYbTM13+pNe3H3n69aSEZy6\nrruwok3alrL87kqe32E3XjtwLNHlnZf7wY+Hs+r+ipbvqTqL/5wxiud32o3XD96Zhk+73s+6wkt3\n7swFI07mD5OPYeOyoi6dc8fZ+/Pa33cAIJ20mHHqQZxV8j0u3vlEFr40uEflA3jhrnGcN+oUfrP/\nN9i4vGsy3nbOgcy5b6eW7288uANXHH0kN5x6COu+KOkx2WJzfFRfXETt5ZEuPcsNM4NU/7SI+ttC\nLc9yM/F5XuquC7d8t+uEqvOKqb64iOqLi6j/S6jH5M7FPziVqfxtq14jF+bfAczp5ZiLuzZ+mxkR\nzGnlmKuL2tShscH8oRjzUev4YqosJ+2McszvizFV3VfvNt5XyoI9xvDJ0SNIrOx8DFv+84FU/bNt\nX6ubE2bx94by+ZlDaHy/Z5/lnsSyZZsd2zP/88aAiFwmIh9mfZ8jImduZhkzN/ecArI0ishGEVkj\nItN6oLzLsr5PEZGkiKzPOvbbUrm3UL6re+G6HhG5TUSqReRzETmgk/wF5RSRWb1Zj82YZR7MgyFk\nRi1yfhPmtk6UhWeCsMFC7t+E7JiGJ5wXrLk/DEUGeWATGDCv+REPWA9sajlkWgNMdrRKs9SL/KSx\n9bcT4l2SN73aoulpP+W/j1J8SpyGBwu/EGKv+shUWfS7pgnvCJvoS45CWfH7KP2ua6LfdU2U/TyG\nd4SNp9LQOCuAb7hN/5saCX41TeMjga7J9YWX2N+KKbvvSyI/r6Px2tK8eeOzwtjrvFQ8vQHvuBSx\nh9rWefzJEOn5rdeN3lqCd2yKypfWEzg8RtPN+cvuLRlNGhqvLCM1v2v11RXqPw7y+Z8HceAbi9jt\nmlV8NG1YwfyLrhjE6ocq2qQt/vMgImMSfH3xQnaatoF3Tx3dY/KtWljGE1dNZPq82Zx+wzv8/aJ9\nOj3n41cG8cb9O7Z8f+bGXfH4bGasfJgT//A+My/s1j/6zMvKheXMump3rpr7BGdeN497Lu58yFk4\nZzCv/aPVEPhs7gCeueUrTL3+LXY5YD23nX1Qj8iWXm0Rmx2g7A9NRE6N0/iPws9yfI4Pu9qi4rpG\nvCMzxF5sNQ7tGEQfDRL5Tus4kl5pEdw/ReUNjVTe0EjJ+bEekTsXT3EMP+ZWDNtWOTPLPfBQGG6s\nhfOa4I5I4ROeDcBGC2bWwA5peNKpc5MBZkTgI1/b/HeFYXIS+XsN7J2CmzopPw/RT/2su7GSXf+9\nghHTN7LydwMK5l9zTSXVs4rbpCVW+Fh16QCGXlJFv5PrWfKDwRjTLXGUbcT/vDHgMl5EJve2EC63\nGGMGAPsDvxSRiT1c/nxjzKCsY26hzCJSJiIXdbXwzc3fS5wF7A2MAC4BHhKR/FOrnWCM+VZn9bhN\neMsPhyaQShvZJQ31gimgl5s3/ciJMcQHHBvHzHNfyG8GkJNjiAVybBzzVsdZXPNQGDk16nxZ4oUx\nmc0WN7HAS3ByCk+ZwbeDjd0omESB/PO9hA9PIl4ITUmSXNBxRir6jJ/IsY6R4h+XIXJiAgmAf3wa\nu6ZrL/fkq0ECR0fx9LfxTUhhai1MLPe5yVdChE5rRHwQPKmJ5GutSpC9ySI6sxjfvq2N4JuUIPzj\neiRk8O+fwN7YvSF2a8qYejeAZ1ga7/gcLoRusv6pUoadsong4DQV+0RJbvKSbsp97w2LgqRqvAw5\nsbZN+tpZ5Yw6twoRGHRsHfH1PhJV3X5s2/DekyPY/7QllA+JsdO+X9JQHSDelH/GMxm3uO/ifZj0\nzRUtaSPG13DmzfOIlKXY/ejVbFoTznt+d3h39kgOPO0LKoZE2XnfjdRXdSajh5k/m8xXj1/Wkhat\n93POjDcZOq6OKWcs7jHPQPJ9L4H9UnjKDb4dM5hGq+CznHzPR+iIBOKF4MFtn+XoEwH8E9J4h7UG\na6dXevAM3/wxpjvcw1R+z/Rtcq02zPPDIQmk0iDjOh+/mRuAE+LO+H10At52x+kFPhhsw7h0S1aT\nAub74XC3UaYkYJGvQ5Fdofa5IipPqsc/KEPRXnHSmzxkornHnthiP+laDxXHNbZJT1V5GD59I5EJ\nCcqPbsQkLOym7XNmXDLb7tie6SvGQD1wXm8LkY0xZjnOv48e28uilAGbo9xvbv7e4BvAX40xTcaY\nx3D6+R69LNMWY6osZHTrC4AKGzYUUJaqLHDzS8RAnfu41wsMdUemATasbVuG+cIDHoPs4OZZ4sVc\nUeyEG11VXPgFlkWmRvAObX3hW6U2mer8Q45dY7UoCFYY7Ia2Lw+7Xkgtt/BPdO4puHcacYtLfurF\nN6ZrK8HsjR68O7bWo9UvQ2Zd7nq0N3rw7uT4560ig13TKn/TDSWEz2zAqmy9buDrcZrNzuQ7Aby7\ntYuP6CJbU0bfvgnCUxs7lLMlxNb4KdmttWMEB6WIrcodKlQ0Ns7461aDdJwqTFY7SmN0hR+TErzF\nPbO6b9PqMCPG17R8Lx8co3pl/pnTJ/40kb1PWMHw3VrP2f2oNYRLnXr++OXB7LjPlz0iW4uMayKM\n2G1Ty/eKwVGqVub3/s26ane++s3lbWTc48jVjNmzmmTMw6NX7cE+JyzvEdmcZ7NVm7HKOnuWJeez\nnF5vEX/ZT2ajRf0dIVLLnTLSKz3Eng1QfVERtVeGyXQSQrMl/IuTqKR6q5WflyoLRmWP3wY2Fhi/\nq1vztxm/90whJ+fwnKSBZoV7hQdKuvfsJNd5Ce3Saun5BmZIrs5tWAR3SjLyyo1gtX2Wi/aKU3pg\nFDsFG/5WRnhCHE+Ruga2Z/qKMfAIcLyIlDcniMjMrM9z3BCb5SLygIisE5Gr3HCeM9xsh7ohJ4tE\nZFLWub8RkZUiskJEjstKXy4ih4jIKyJyb3uBRGQEMAn4zP0+VUSWutf+QVa+P7ohRStF5PTuVoBb\nzlq3/B+5aQ8A7wLD3ZCiZwtdt5P8OeXPI4slIte55X8gInu76WERecwtY6GI7OGmzxSR34nIXBHZ\nJCI/d9NDbv71IrJYRPZ3LzEGWJF1yZXAKPecgu2SR945IjIl6/tlInKDiDzrhiLdlPVbh/7g3u+d\n7n2tFJETOrum/cfiDjH8pAXCWQNq0LQO/jkLaZe/+V0eAGk+LWig3QyueSaIHOUodiYO7JdArqlD\n7t/kvHCe6hjPW3tLsE1cf9W0CGQECbVeXwJgooVuGqys/Kbduyz2ho/gfulW2ZtPaxBiL/oIH961\nmW6TAYm0Fi4hg2nMXY/Gdl/Ezbh1mJwXwK7xEPxG7nAGu9Yi9mCE0GndU7q3pozt669HyIC3uFVZ\n9ERsUrW5FZ181x92cg3zzxrJF9cP4O0TxzDw6Do8gZ5RIGxbCBW39o9AJEVTbW5jZdXHZSz49zCO\n//WHOX9PJSxmXb47x05b2COytciYEUIlrcZjIJIuIGM57z87nG/9akHO3289+yCevW1XDjt70WbL\nUT8j1BK733yYNEhWZJAEDCbPbDGAMW2ffdy93GPP+fEMsAkenMQ7PEP9tRHsesHT36ZR4fR3AAAg\nAElEQVTkR1Eqb2zEv1eaxod7LoStPdtiftpcUeysDcg6yLCZ4zc5x+9cz4/4cMI6ryjGPByC64vg\ngG56/jKCJ8sIt8I2mfrcqmJnY8maP/Vn9fT+DJhaWzhjL+KxZZsd2zN9ZWvRKuApoCvK9DOAH+gP\nXA5McdPHAeOBo4B7cEKPjgIOBXYBhgFzRGSEMS1Lfa4FfgJ8kFX+BSJyFlAK/MoY84GIfAX4GY5x\n4APmi8iTQAD4GrCzm38+cF8n8u8pIuvdz3FjzCgRqQB+DQzEMQDvBG4zxpwqIqOAOcaYUc0FuIZK\nh+sWyJ9P/nycBewO7ADsBzwiImOBI4B1wBDgO67MJ7vnnIfTFhXAizh1exQw1M1/IHAY8CYQBLKd\n2EkgW4PN1S6by1nu9dYCS0Xkj8A+5OgPOP3mWGCke88XAo+1L1BEzgXOBZALr0MuPaPN7/Zfw9CY\nNSgnpfCbrch28gfdgb353WAZjHEH8oS0maE1KeAdvxPTiqMAyE+aWss8Io6ZHUJOaqtgll3Q0V3Q\n+EigrcKQoqC8EjbYUWlVAHMsOiz7aUflu+HBAMH9Um28EIWwSgx2Vj2aRP56tIpt7AbB06zYJB3X\nftPNJZRcsyn3SUDjNaUEj43i3SGdN09vy9iT+MoypOpalf9MzEKszVPkx122lqKxcWr+E6bxsxB7\n3Lmi85O6SKQsSVNdq2KdjHmxcshnDMy8cF+m3vIWXn/u/vToH3dnxMQaJhyxtsfkc2RMEK3NltGT\nV8a//mQ/zrn5zbwyXvzAy3z40hBuOPUQbvrkkc0yAEsu7PiMNf0zgJ2VbDoZe6w8z3J6qYfwiQkC\nezjPRWqhl9RnHiIntQ7XoYOTRJ8obl/kfxXyu4YOaeZv4bbKf4LC43fEnahpHr9TnTTiRY3wQsAJ\nD6qz4BtddOG2w1OaIVPXOvbY8U7eMwUYfumXVJ5cx+LvDmP8m8vUO7Ad01eMAYDbcZTgXL7d7K7+\nFo6S9xaOLd78VNxvjIkDj4nI/SJS4ubbG1ji5gnjKKbNb7H/M8a80e5at+AYGauAp920Q3Bmsz9x\nv4eAscaY19z4/GnAwTjKfGfMN8a0X9lWh+OBuAF4Fvh+oQKMMSs387o55S+Q/yjgLrc+XxaROmC8\nMeYxEYnSquhvzDrnXmPMEhFZCjQHwn4ADAf+BLwMLYGgURyDoJmAm9ZMrnbZXGYbY94FcI2vQv1h\nCc48zzWunDnDrIwxd+L0UTzPVnUYNWWnNOZjH3JIwlmM9YUX+hVQgHdKw6de+FrS2bmiebJtdAY+\n98LOaedvdhkLfDA2jbh5zQYL1niQPd23ebXVZX+id1SG1OcegvumMQZSKzxY5flfBr5RGVJLPXj2\nSpP5UpCsSdH0Ogvxgaey7fmxV31kNlqUnNX1F593lySpBQE4MoYxkF7kwxqQO6DTu0uK9Ed+PIfG\nnR19gob0Ij/2eg+1Z/cDwDRZJF8PUvTLWgJfjxObFcZe7aH4spqcZW4PMvY0pXtG2TS3iGHfqcEY\nqPsgRHDI5oVIiQXDT9tEusFi4DG1lO3Zc4tIR+9VzWdvDGT/U5ZhDCx/v4LyIR3dVNWrIqz8sJwb\nv30IAPFGL5bH0FAV5JiffcwHzw7lnUdHMf3t2T0mWzNj9qxi0ZuDOOCUJRgDyxb0yy/jRxVc+53D\nXBl9rowBdtz7S4bsXEfpgDgTDl1LY02ARNRLMNI9o7QZ51n2gvssp1d6sMrzjz3eURnSSzx4JjnP\ncvPYIyGDpyLrPJ+TFp/rI7if01/sWgsRWics/lfYKQ0f+2BK0hm/l3ihspPxe5EX9m8evwsr0uIH\njklg1nvgqDhSqOwCRCbGaXg7TOW3GjAGoguD+AdvXv+JfhzAitgER6UI75LENzBNco2P0NieW6fU\nU+j/GXDoM8aAMeZNEcngxI7PbPfz0KzPmXZ/W4po99nCMSKuNMb8GZzFtUB2XMC8PLJEReRvwI9w\nFEMB/m6MOd8tpxhIiMjXXFkvBe4Flnd2n3mul3FDcQ4DjgGmi8hXjMm1CSF047o55ccxEvKK1e6z\nEZHfuufcBLxOW6V5iXsvRtw3hGscTMDxKPwcOA3H0FmKGxbkMhJYlvU9Z7tsJkuyPjffS87+YIxJ\ni8iuwOHAqThelM3f5mNSCu6KYCamYLkTEyr9849kMiWBmVEEpTbm8RBMTrSm3xGBqU2Ye8PIlNZZ\nOfMfPzKxrRJnbimCq+ucz7NDyHFdU9L8u6Vp/GcA37gMmTUWVpHBU5H/hRb4apqG+wJYRYboiz78\ne7S+gJIfefCNa/tIJr/w0PiEn4pfR5HNGMl8kxM03VhKYlKCzBIvVqmNZ2DuegwcHqPx6lKscpvY\ngxECB8Xx7Z6k8uX1LXka/lhG4Ngo/r2SpD7wE72jmLK/VTmu+26yNWXcGgz4ej2f/Hoo/Q5soOHT\nEP6KDKFhm79ewk4KS28bwN4PLe1R+SYesYYHLtmbXQ9ex+qPyyiuTFA5vKOi3W9EE3dVPdjyfdb0\nifQb2ciBZyxhwxfF3HH2AUx7/CUiZd1bC1KI3Y9YzX2/3Ifdpqxl1SflFFfG6Te8qUO+fiOauGdj\nq4P4kSv2oP/IRqac/jmP/ml3XvzrOH5892ssnDOYiiHRLTYEAHzj0zQ9HCQxzus8y5HOnuUUjfcF\nsYoNsRf8BHZ36su3c4b4Wz4iIxJkVlukl1t4R2eo+1MQq8LGOyJDdLYf/+4dwwH/69krBX+NYCak\n3Jh+U3D85sAE3BrBlNjwZAj26fzZNfUCrwXgpu6H5ZQeHGXVHwdQsn+U2Gd+vOUZ/EM20xj41E/1\nw6XsdN8aEit8pL70Ehi9/RkCSit9Zc1AM3/BmcGtwJlRRkSOxgnd6IxTRCTgxoGvMcbU4oSrnCwi\nJSLSPANc1kVZbgFOF5EIzmzxUSIyyFWkPwB2xQk7eQd4EDi6qzfZHhHZGXjJPX4JDAIq3Z+rgUo3\nXj8sIqFOrpsrfz758/Fv4Gy3Pg/CqbOFwGSc9R0vAu3j6jvOlItMBX4HPABcDTR7RJ4EzhGRIhE5\nCSdAJndwbffJ9SbM2R9E5FDgb8DjOKFPXxXZ/FedRAzy2wbMrBDmIx9yiWN3mk+82H/ouGuIjE8j\nJ8YwdxRB2CCnOUq8HJpAvprE3FrkeAeOyZop/sjXZpcKGWgjJ8UwF5RhppU6calHFNhGJAsrBKU/\njBF7wUdysYeSc5zrp76wqL2547oD/84ZwoenaHwogBWEyHGt10ku9uBrt6NR9DkfJi7UXO3+H4Jf\ndG0rPavIUHzVJmIPREjND1A0vYbUhz7qLq7okNe3Z5LQaU00XleKhA2hczq6/7OJ3R/BRC1qz+5H\n9ZED2XRsV5x521bGrYGvxGav+5ax5JYBVL9RxJ5/Xc6meRHePnHMZpWz8t5KKiY3UrJrz3ovQiUp\nLnxwDs/etCuLXhvE+TNfZ/Fb/bn2+EO7XMYLt48jWufjhhMP4YIRJ3PBiJOpXddz++GHS1Jc/MDL\nPH3zbnzy+mAuuOdVPntrAH8+4fAul3Hczz7CzlicP+YUHr50Ej/5+ys9IpsVguLzY8Se95P6zEvR\nuc3Psoe6GzvWgW9shtARSRofDCJBQ+gbzrMcPiaBiQo104qovzFM0elxrAgUTY3TeG+Qml8VgS1E\nTt16W4v2FhI28OsGeDwIC30wzXlOzadezB87hkXJbmk4IQ53RSBk4JRCC65cHg3BEXGkrPvhOJ5i\nmx3uXMuGu8ppmBdmzIx1NP4nyOdnDOlyGZUnNhAam+DDfUez5EeDGXPrOqye+5cmPYqVkW12bM+I\n+R/f/FWcffiDxphfuYrqWhyF+FSc5ZALgYnA73Fmw6cAlwFz3CKmuH8rgAk4iuWZzVtNisjvgR/g\neBJ+a4x5wE1fDkxxdw3qIIv7/THg38aYO0XkbBzF1gvcaIy5zo3Pnw0MAP4JnAnsZYxZnFUexpjL\n3O9TgOeB7EDhq40xN4qzj/6Zbto9xphfZ8n1O+DHOMbhZJyQlkLXbZPfGLM0j/yXAb+hNVod4Hs4\nyvo1wHdx1nOcY4x51zXM/urmfwLHIBgN3I2zTmGme33XQSDFwD9wjIAo8AtjzCMiYgEzgFPcuvi+\nMebNTtqlg5zGmMfd3+cAlxlj5uSp95Yyc/UHEfECd+EYVmm3TWZQgFxhQtsb/VZvvUV+PYE9cdsr\nxf9rTN5lQ2+L0Ckn+3Mv9N1e8NlbPjO/tbnggW/3tgid8uXZ3fufHdsKa3ZVb4vQKXvt0Qu7KG0m\n7w7deZtqzeX31W2zd23N6aXbrUXwP28MKMp/I2oMbDlqDGw5agxsOWoM9AxqDGw5agx0pOLe+m32\nrt30/ZLt1hjoa2FCiqIoiqIoiqK4qDGgKIqiKIqiKH2UPrObkKIoiqIoiqI048m9Y3OfQz0DiqIo\niqIoitJHUc+AoiiKoiiK0uew7O12Te82RT0DiqIoiqIoitJHUc+AoiiKoiiK0uewdM0AoJ4BRVEU\nRVEURemzqGdAURRFURRF6XOIrhkA1DOgKIqiKIqiKH0W9QwoiqIoiqIofQ79PwMO6hlQFEVRFEVR\nlD6KegYURVEURVGUPodl97YE2wfqGVAURVEURVGUPop6BhRFURRFUZQ+h5XR3YRAPQOKoiiKoiiK\n0mdRz4CiKIqiKIrS5xBdMwCoZ0BRFEVRFEVR+izqGVCU7ZAfHv5xb4vQKevjkd4WoSBvPDC2t0Xo\nlLPPmdvbIhTkhlf26G0ROuWFTaN6W4ROSXtNb4tQkGlnzettETrFGlDV2yIUxD6uX2+L0CmV89b2\ntgidM3TbXs6jawYA9QwoiqIoylZjezcEFEVR1BhQFEVRFEVRlD6KhgkpiqIoiqIofQ4r09sSbB+o\nZ0BRFEVRFEVR+ijqGVAURVEURVH6HJatC4hBPQOKoiiKoiiK0mdRz4CiKIqiKIrS5xBdMwCoZ0BR\nFEVRFEVR+izqGVAURVEURVH6HPpPxxzUM6AoiqIoiqIofRT1DCiKoiiKoih9Dv0/Aw7qGVAURVEU\nRVGUPop6BhRFURRFUZQ+h2X3tgTbB+oZUBRFURRFUZQ+inoGFEVRFEVRlD6H6G5CgHoGFEVRFEVR\nFKXPop4BRVEURVEUpc/h0d2EAPUMKIqiKIqiKEqfRY0BRVEURVEURemjaJiQoiiKoiiK0ufQfzrm\noMaAovwXs/D2wbx7+SiKhiU44uFPKBkdz5kvkxRePH0cy2f3IzwoyZQ7P2P4YbU8882vsOGdkpZ8\niU1evvH8hww5sG6ryLv0r/359MohhIYm2ef+JURGJXPms5PCu2ePZt3TZQQHptjz1uUMOKShx+WJ\nzfHRNNuPVW4oPS+Gp7/Jm9fY0HB/gMT7XvxjM5ScHUd8rb/H3/YSe9NH+c9iAGQ2CQ33BkmvsfAM\nsSk5M46nIn/5hZh/+zDeuGxHiofFOeGRDygbHcubd+E/BvPRPUMJlqc46E+fU7FTtOW3Z875CsMO\nqGHCmWu7JUd70k8GSd8bQfrb+C6twxqce9Nuk4H0DcVk3gxgTUzi+0094nd/syH1q1I8p0bx7J5q\nc17q2mJktxTeI3P36y7L+Zyf9CMhpMLGN60Ja2D+zcVNBtJ3hsm868PaNY3vp00t7Zx+OoD9jg/6\n2fhOjyFlpmD65mA/G8B+KASVNp5LGpFOZLRvD2Pe9iO7pbEubkR8YBoF+/+KMJ94YZCNZ1ojMjqD\nWeQlc2kxhBy55MAEnrPy96Gu8P7tw3jzsh0oHhbn+Ec+LNgnP/7HoJY+eeCfvmjTJ3sS8+8APBCG\nfjb8sgEZVLgOuS0C8/wwPgXTGlva2djAZcXw7RgyPu2kVVlwcwSWe2FkGn7ahPTbepvU/4NTeZHD\nuIeztto1sonPChP7axHWgAxFf6rFMyS3lmwyEP1zKcnXAnj3TFJ0WW3Lsxx/OEzy1SDWoAzhHzdg\nVdrUndEPu6o1CMXUWpTO2oinQNso255Ow4RE5DIR+TDr+xwROXNzLiIiMzf3nAJl/UBE1orIehH5\ncbvf9hGRRSKyUUR+ISL7ufnqRCTmfv5tVv7viMhNPSCTV0QWisj+WWn7i8hiEVkmIkd2oYxzRGSF\niGwQkd9kpV/hpn8uIt9y085076dGRN4Vka9t6T20k2WKiCTd+tooIjeJSI+FlPWU/G45X4pIwP0+\nU0Qu60Y5a0VkhIj8S0R+IyLfFJGnuiPTtqT6owjvXTmSk//zHl+76Qteu3DHvHkXXD8My2c4c+1c\n9r5sOa/9eCcAjn78Y6aufYupa9/ilIXvUjI6zoCv1m8VeesWhlh09WAOmfsJE69byYKfjcib9/Ob\nBmJ5DUcv/YBdfreW9y8a2ePypFdbND3tp/z3UYpPidPwYLBg/tirPjJVFv2uacI7wib6kr/lNzsO\njbMCFH8n0ZLW+HCAwB5p+l3bRGBCmvp7C5efj40fFfHm9B2YOv8tvj7jU56/YJe8eVe/Wca7N4zk\n6zM+ZfiBNcw+Y3zLb6/9YUcW3jekWzLkwl7qIX1fhMAdNfgubCB9c3HevJnZIcwGD4F/VmHtnCYz\nKwy4ive1xdgf+Duck7onQuaF7tVZGzlXWKQfDRH4v3p8Z0dJ3x0umD/zfADzpUXgjjqsMRkyTwec\n9Nf82O/78P4wipQbUveEC6ZvDma5B/ufITzX1+H5QRT7zsJlmOcCsNGD5+5aZEwaM9upJ/vvIRiT\nxnN/DdbXEtgzwy3lW9+O4Z1Zi3dm7RYbAl9+VMTc6WP4/vx5HDZjES9cMC5v3tVvlvGfG0Zy2IxF\nDD+whqfP2G2Lrp0Ps9wDD4Xhxlo4rwnuiBQ+4dkAbLRgZg3skIYnnTo0GWBGBD7ytc1/VxgmJ5G/\n18DeKbipk/K3gKc4hh9zK4Zts+1l+gsvsXuKKL23ivC0eqLXluTNm3gsTGadh7LZG/GOTRF/2KmH\nxLNBUm8FiPy6DqvSJnqjU0bp36sof2Yj5c9spOS2ajxjU9uVIWBlZJsd2zNdVfDGi8jkrSpJFxCR\nscCfgSnAfsB0Ecl+M/4KuNIYMwD4mzFmrjFmEPBT4AljzCBjzJXNmY0xDxtjftoDol0EfGyMedOV\n0w88DPwQmAT8WUTyju4iUgn8GNgFGAd8X0Qmicix7n2OAw4HbhGRUe5pTwD9gNuAp0VkQA/cRzbz\n3bobDewBfLe7BeVR0HtK/n7AtzZDljIRuahd8kpgADDCPQYAq7opzzZj2ROV7Hz6BiJDkgyaXE+8\n2keqKfcj3W9CEwfe+jmBsgyjjq2mcXWgQ54PbxrGV85fizfYvdnrzlj7VBkjTq0mNDhF5T5NJKu9\npPPIWzo+xu43rsRflmHwUbXE1nRUFreUxAIvwckpPGUG3w42dqNgEgXyz/cSPjyJeCE0JUlyQatj\ntemJAIHxabxDnZecSUPyYy/BA52Z7uC+KdJLPN2Sc/HjAxh/xlqKhyQYNrmOaJWPZFPusuJ1Xo68\n4xP67dLEhKlr2LTYGXaqPokQ2+Rjl++s65YMubDfDOA5PI70s7G+ksbUWZg8Oqb9egDPyVHEB55v\nxMi86bSn/Z4fGZpBdm3rEbCXe6BesKZsmUcAwH7Hj+egBFJhsMZmMPWCKVCs/bYPzzccr4/niDiZ\nd5tdGOD9QRRrsI3nawnMOqtw+mZg3vYhBzsyyrg0pt4qKKN5y49805FRjko4XglAJqSxTo8hQZBJ\nKUy1I4tZ6oHRPRcPsfjxAezm9smhk+uIVfnz9slEnZcj3D45furalj7Z48zzwyEJpNKpQzppZ+YG\n4ATXu3d0At5223mBDwbbMC7dktWkgPl+ONwdIKYkYJGvQ5E9xT1M5fdM32rltyf1WpDAUTGs/ja+\n8SnsWgsTy628JucECZ7meFECJ0ZJveYa7EYIX1KPZ3gG/xExMis7Bp7E7i0iNLVxa96K0k26OmrV\nA+dtTUG6yLHAc8aYxcaYpcCzwDFZv5fjKnHGmOptIZCIDAEuAX6ZlbwPsMoY87Irx0vAAQWKGQMs\nNsZEjTE1wIfAUOA7wN3GmJgxZhnwHo5xAYAxJmOMucdN77JCvDkYY5qAR4At8T5cmqfsnpB/c/tm\nGY7xls0qHAMgCgzhv8QYaFwdoN/41oE1MjhJw4rcM6kjj95EoNRRBla/WM7AfduG3GSSwuIHBrDr\nWT2nLLYntsZP6W6t2mJwcIroytxK/qAj6/C58m58pYSKr/b8CyRTIy3KO4BVapOpzj8k2jUW3mFO\nfisMdoPzskyvF2Kv+Eh/aVF3Z5DUclcBy4BxoyHSazxIUfeMrIbVQfpPaG2v4iEJ6vO0845HVzF4\nr3pSMYs3p+/AuJM2AFC5SxNH3PIpPeffA/OlB2tMq8IklTZmQ26F0HxpteSVIgN1jiDW3km8p3UM\nGZGRGXw/beyRLS7MJgtrZKsiLBU25sv8BWfnlwhQ77Sz56Ak1kAb0wTpx4N49k0VTN8sGastpJ2M\nFJKx2kJGuvUZMVDv1ucBScRtAvOBD9nZDXFZ5sW+O0z6jDIylxZjardslrJhdYD+E1qfyaIh8bx9\ncoejqxi0VwOpmMXc6WMY6/bJHqfKglGt/ZEKAxsLGODVrfkl0ton2TOFnJzDqk0DTW69rfBAydab\n3f4XJ1HJNlFhALA3Wnh2bO23Vn8be33uurM3evDu6NSbVWSwa516CxwVwzMkg90oxO8rwn9I2zq0\nqy3Sn/jxfa3AjEsvIPa2O7ZnujrUPgIcLyLlzQkiMjPr8xw3tGS5iDwgIutE5Co3xOQMN9uhbqjL\nIhGZlHXub0RkpRsKc1xW+nIROUREXhGRe93kMcCKLLlWAqNE5HgRWY8ziz7LDW8Z29lNuWEm2fcx\nU0SeEpE1InKNG+Jzp/vb0a7sG9rNdF8NJIAZ7r34gGE4Cn0zK4D8MRywBNhPRHYWkV2BfYG57m/9\n3Ot7cDwHVTnOX4DjPUBEfujW5xoR+aWbdoSIvJJ1nzeIyC/cz+eJyCq3ra7IV1WAna/8fOWIyJ/d\ndsFtk4/zlL8AGOf2oTlZZbaElxWo/xeBnUWkjZ9aRL7ntt/yrDIeAN4FhrvyPOtmX4XTZjU462gG\n4PQtxAk3Wykin4nIUVnl5+qfiMgf3bCjdSLyo6z0qSKy1E3/QZ562CxMRvCVtCoQ3kiGRG3hZUCZ\nhPDOH0ey5y9Wtklf+mg/hn+9Bl/R1huxTAa82fKGbVJ1hWfLMwnh0yuHsPPF67fo2rW3BKmaFmlz\nkBEk1KqgS6BVec+JDVZWfuNWVfR5P57+NuGDUniH29ReH8ZEhcAeaepuDdH0tJ/6u4MEJ6XzFFwY\nkxECJa3n+iIZ4rWFZyWfOmM8/7l5BHuc59i0sjU81DYQyTJwQgYa81zIBsJZfct1meeTq7vyJq+O\nED+ntM1BmpZYeQACplWpyydrdv52E+rJ6cXYC3x4vpbsUnp7Mn8qIn1mWZuDjEA465pBA3m8Zi0y\nhvPLaOoFe3YQ63hnalxGp/Fc2oBnZi0My2D/M1RQxs7I3ScLjz1Pn7Eb7908gt3PW71F1wYwVxRj\nTi9vc5AhRx120s456jBX3xMfMDkJVxRjHg7B9UVwQOF23hK2eUBJRhyDqPn6QdMy2dEBmzZ52/e9\nhp9UkJoXwH94W7dM4qkQgWOiW2csUraYri4grgKeAk7vQt5nAD/QH7gcJ6QHHGV1PHAUcA9O6NFR\nwKE4Su4wYI6IjDDGNJuo1wI/AT5wvwdpqwwngZAx5gngCVeRvMwYM6eL95WL9cBVwIXACTjGRX9g\nBnAwjsI4T0SecGU5DSd06S3gD0AJsBBnxrqZJmCAiNwCnNTuei8YY04XkedwQmfSwEPGmC9F5EHg\nPhEpwpmZDwNvAKPaldEIDBaRIE4b7QvUAitE5FYcz8R9IlLpeiqOxQk7ArgGx4j6ws3TJvBXRCqA\nU4Br8pVvjGnMVY4x5pfAL0XEuCFH+WgEBuf7sUD9A6SAu4Fzs/KPwzHSJuMMVfNE5D1jzKnihFnN\nMcZk1+FKHI/LSmAHXM+AiBwGfB+Y6Mr3sohMNMY0T2216Z9uXf0aGIhjaN8J3CYiXwF+5l7DB8wX\nkSezymmW+9zm+zjoL9P4yrnHUYhAeZpk1gs4E7MQq/Ds8zuXjqLfxCZGHFnTJv2z+wey5y+3rjPE\nX5YhVduq/GfiVqcz1Z9OH0Lp+CiDDt+ydQxlF3SMF2h8JICJZr2ZUhR8C0vYYEcFT8C05gfSyzwU\nnZgksHuaAJD82ENysYeSqXFib/hILfFgNwihQ7unPATLU22U/3TM02k7n/DIByx7oZLHTtqdHy55\nfeu8gIvstsp/gvzTS8UGGi0IuWFUW0mP8v+qqUNa6u+htop1UgpPg0VcJdINlzOptpUXuLqB9At+\nkjPCBC5v7DS9PZ7fdPwtMzPUVnFNCEiBNi5qKyPJtjLad4aRQxIt3gbPj1qtXOvIOJk/51/f0RWC\n5ek2yr/TJwuf881HPmTZCxU8ftJEzlvyxhb1Sfldx80EzN/C7eqQwlp1xDW4gq6RmupEoIsa4YWA\nEx5UZ8E3tjyEbXtBSmxMlvJvEuRtTyl28jZPpJh2fa/0b9XEHw/RdHkpJX/Z1JKefDZE0Q2b2N7Q\nfzrmsDlO2NvJH46R3RvewlHumv82X+N+Y0zcGPMYMEZESv6/vTsPj6ssGz/+vWfLzGRPuqYtOxSQ\nAqUFCipUFLDIJijI8vqCLL6iP0VBEAEFKoKoKCrwwqvsa2WXRWQroKUitBRaCpRudEvTplmbzH7/\n/jgnySSZmTRN2xk69+e6crU588yZe55z5sxzP8sJ8CXgQJye8VeBUpxpGl1uUNV/qmrXJ78DJyHo\nUuJu25K64n4Tp8HrwWn8jnG3LXJj/AxO4+5dVf2pqv4NuBg4DqfBWpW2zxCQUtXvuesW0n/+S0S+\n4O5/b1WdAAwTka+p6rM4yUMFTj39VlUzdS+WAu2qGgG+idNgnwHUAMPc5zwLHFjgOl4AACAASURB\nVOeOPDS7047ASS6uxWnwfyetrg9we/Xn4vS+P55t/wPsZ1OU4tR5X13nVbb673I7cCY958aRwNOq\nukJVVwOPA0fneP0VOMdyOc6x28PdNg3nvG1S1feBf9N7ulTf87MF+BD4nRvDf7vbj8AZ1XofJ3Eo\nA/qNXKnq7ao6WVUnD5QIAAyf1Eb97Ar3ubBubhmlY7K3spY/V8PiR4Zz+C0f9doebfHS+G4Zoz67\nde4g1KX6gI1seLOsO97md8IE67LHW/98BSsfr2H/mz7JWmYofDsliS/xdscTX+7FU529AeZPK59c\nJ9130JCg4qnp6fUWP3jCivgh/IU4ngoldHgcb4595zJqciur3qjqjrN+TjnlYzIPta94vYqNDU5g\nOx/ZSOcGP/GOzVurMBDP+ASpBf7uuHSRP+vdVTx7xEm975RNrfEgJZtXF5sV564JUh/1HGdd6nWm\n4WQtnyT1kdPQTa3tiTU5x9e9psR7aBxd6c25fTBktyT6oa8nxiVeZ5pL1vKJnvJrPc5ohyv1fAla\n78XzDWeahrYJqTd6kklt9MAAyeRARk1uYXXaObl2TgXlYzI3jnufkxuIbK1zcvdE9zx+VWCxD2pz\njHTunoAP3Dqs712HmUgA5CtRqErBtAiSa9+fMt494yTmO8dIFZIf+pHhmVvJvrSyydVexE1IY7NK\nutdoBL4YIbk0raNqqQ/8FNTCYdPbJicD7uLYJM5i0r7GpP0/2eff7l30+b8Hp7F3bVfDGGfx5qq0\ncrP77GMJvXvFdwSWsmVlil+AV9LiHAs8itP7n95SieAkEG/jrBvoMhnINTY6BfhAVbvq6C2cxj+q\n+gpwn7vvW7M8f19ggYjsCrwGbAAuove890eAE4HjcaZ9dTke+ANO43S+2wsP7gJiVd1RVa9UVR1g\n/9n2syn2BTJNIeo6r7LVPwCqugLnXJmW9ty+51uuK/0KnPN6mfuzPz3HK9d+ep2fqprEOW6PAIcD\nc8VZTC7APX3i73tuD9qO0zaw9IlhLH5sGG/9YkeCtQnKx2VuJLZ8HOSls8Zz5AMLKanq/dFc+VI1\nI6e04tk6bcZuI49qYfVTVax6sooPrh9NoCZBeGzm+dXti0t46/ydOejuJQSqtk7XTWCfBNG5PiJz\nfHQ8HcBTpjlv/VlyUIKNTwSIfeilbUYJgYlOXu7fI0nkDT+qzh2K4ss8+Nwe2VQ7RN/0UXrM5neF\n7zptHR89PoIPHxvBv6bvSqg2TsW4zA2v5a/U8uIPx5NKwtIXaikfEyVQunXqz3NQjOQ/S0i+VkLy\n3jBUpJARmb/svUdESdxZSmqen8Qt5Xg+u/WmWPSLc2Kc5JsBkrP9JB8JQpkiw7IfZ+/nYiQeCpFa\n4CNxdwjPgc45mnojQOIRp78hOcuPZ7dkzu2DIQfE0NkBUm/40RlBKFdkeI6E5fMxUg+E0fk+UneE\nkYOd+tSFPlIPhPD+uOc2mQSU1J/D6HIv2iLo4yHkoMGva0i3y7T13efkrOm7EKqNUZHl2vPJKzW8\n3H1O1lC2tc7JSXF4I4DOCsBDIajIXYccFoX7Quh7PvhLKRw88DmprQKvlcDXh3Y3pkITODRK7NUg\nsVeCRO4oQypTeLPc2jZwVISO28qJzwnQcVMFgcOca1Hs5SCddzgjTrGXgvjSbgoQe6ME/+TCWivQ\nxe4m5Bjs8qxbcXqpa4Bx4MzlxplaMZDTRKTEXRewSlWbcXqcTxGRCnEW4i6md496X08DR4nIeBHZ\nBWeqyzODfA+bYzYw0X3dAPCC+9pz3O27uHP6LwBeVdV6YJ2IXCEiJ+Mscn4hx/6XAseKyEEicjDO\nCMz8tMcvA36h2ntGs4h4RORMnMbrY/Q0aO/AaZSPTSv+Ak6CcgpOYxVx7nA0330fP8Ppnc+1tiHj\n/jdhP40isqOI+EWkMkf8rcBYcUzAaVBD9vpP13Vudr3XY0VkjIiMxpnu9Y+uWIBaEQm7PyF6Errl\n7s9aVY0BzwFniHMHoj3d+vtntsoRkT1wpmS9hLOgfBRQC7wMTBORUe40rHnA3tn2s6kCFUmOnvE+\n834/ltWvVfKlexeyZlYFTx/b/9Z9790yhliLj2dP3Ic76w7hzrpD2LjG6d1Z/VolI6dsnduJpvNX\npDj4/sV8/MeRrH+9nAPvWELj7FL+dVL/U27JbSOIt3h54+u78czO+/HMzvvRuWbL3r3DE4LK/+mk\n8wW/M63nXOcLPv6xh+Y/9J9THdgjSfioOO0PleAJQulxzpdb6TExtENo/HEpzTeFKD8zise962DH\n30sIHuaMDmyukookX33kHd68cSc+ebWa4+9/l5WzqpjxlQP6lZ1yyVI0KfyxbiqvXr47Jzw0L8Me\ntwwpVQJXtZB4JERqXgD/T1tJzfcRu6yyX1nPfnG8p3QQ/1MZhFP4/rv/dJ6tFmcYAhe1k3g6SGqB\nD/8PnNdOfeAl9suy/rHuncB7fIT4nSEIKj53ManvvzrRJT4i36ok9a8A/vM35tw+2Bi9l7STeiqI\nzvfj/ZEzUKof+EhO7x+jfCaB54ROkn8OQ0i7RwFSTwShU0heWuGsRzinCikBz/kdJK8uJ/mDShiT\nxDPExmxJRZITH5nHf27ckRWvVnPs/e+xclYlf/1K/77Cgy9ZRioJN9cdzuuX784JD72bYY9DJ2GF\ny9rgiSDM98NFzoCtLvShV/efFiX7JOCrEfi/UmeNyGmbMMng0RAcHdmsvyNRyKRUKftlE5EHS4nP\nDVB2dTPxd/20/ai6X1n/xBihM9rp+F0FEk4ROsc5V8PfayXxoZ+maSOIvRgifGnPSHNibgDfPkNL\nQM3WJT2d0VkKOIs1g6r6E7chsxqnoXM6zvz2+Thzqq8E7sJZI3AVMNPdxVT33xqcHuAYcJaqznL3\nfyVwHk5P/OWq+oC7fRkwVVWX9YnnXGA6TiJzlaremvbYTDKsGXAXkH5ZVb+RYftUVT3L/f2uPnFf\nhTu/XJzbfP4WKAceVNWL3OdMA27AmVM+G/iGqra7jdA/4iRKP3OnEWXkJhK3AF9339cjwPmqmhKR\nfXB6wffpWkvhxn0rzmjBfOBCVX1bRKpwkqPxOI3fA4Hvquo/3OfdD4xX1fQF3N/HuRuSH2cq0bk4\nU2GuV9UpfeLMuv9M+3F7yhGRb+GsHwniNMx3zRK/4CQFI3FGgUqAZ1T1rkz1n35cxfk7CB/j9MBf\n5SYZ03F65a9S1bvS3scVOLdy9eCsK1jqxlKHk/D8UlUPcsv+GGf9SAT4gao+525fRubz83rgLPfX\nO1X1Mnf7OcAVOOt0fq+qvyWH76ZeLfhvm/rI1rvP9pbwzwcGvIdA3p1z7qyBC+XR717JNBBcYDZs\nvVs8bgkJX8F/lLnohLfyHcKAbnh6Ur5DyCl13LCBC+VZ7ewt88cGt6bGg0dv0y70L5wT32Yf0Ff+\n4i/Y4YEBkwGzfRARH04Sl1DVX+U7HpObJQNDZ8nA0FkyMHSWDGwZlgwMnSUD/Vky4NjUuwmZT783\nce7yNDXPcRhjjDHG5J3H7iYEWDJQNFS1/wRjY4wxxhhT1CwZMMYYY4wxRafQ7/KzrWzBP05vjDHG\nGGOM+TSxZMAYY4wxxpgiZdOEjDHGGGNM0bEFxA4bGTDGGGOMMaZI2ciAMcYYY4wpOjYy4LCRAWOM\nMcYYY4qUjQwYY4wxxpiiYyMDDhsZMMYYY4wxpkjZyIAxxhhjjCk69kfHHDYyYIwxxhhjTJGykQFj\njDHGGFN0bM2Aw0YGjDHGGGOMKVI2MmCMMcYYY4qOjQw4bGTAGGOMMcaYImUjA8YYY4wxpujYyIDD\nkgFjCtCjc3bLdwgDqqqM5TuEnA56JpzvEAZ030F75zuEnHaaVZ7vEAY08eL38h1CTrMWjcp3CAO6\n7Y0J+Q5hQJMmNuY7hJxqZ6/OdwgDapxSl+8QBqaa7wiKkiUDxhhjjDGm6NjfGXDYmgFjjDHGGGOK\nlCUDxhhjjDHGFCmbJmSMMcYYY4qOLSB22MiAMcYYY4wxRcpGBowxxhhjTNEp5JEBEfkLsDfwjKr+\nIkuZSuAhwAtsBE4FUsAS9wfg/6lqztuu2ciAMcYYY4wxBUJETgK8qnoIsIuI7J6l6BnAjap6FFAP\nfBnYF3hQVae6PwPef9lGBowxxhhjTNHZliMDInI+cH7apttV9fYsxacCM9z//wP4HLCobyFVvSXt\n1+FAAzAFOFZEvgC8B3xbVRO5YrNkwBhjjDHGmK3IbfhnbPyLyG3A+LRNhwN/cf+/ATgg175F5BCg\nWlVni0gS+JKqrhGRe4BjgKdyPd+SAWOMMcYYU3QKZc2Aqn47/XcRuQkIub+WkWNav4jUAH8ETnY3\nvauqUff/bwHZphh1szUDxhhjjDHGFI63caYGAewHLMtUSEQCwF+By1R1ubv5XhHZT0S8wInAvIFe\nzEYGjDHGGGNM0fEkJd8hZPME8LqI1AHTgCkisjdwuqpekVbuHJwpRJeLyOXArcA1wAOAAE+p6osD\nvZglA8YYY4wxxhQIVW0VkanAkcANqtoCtABX9Cl3K04C0Ne+g3k9SwaMMcYYY0zRKZQ1A5moahM9\ndxTaqmzNgDHGGGOMMUXKRgaMMcYYY0zRKeSRgW3JRgaMMcYYY4wpUjYyYIwxxhhjio6NDDhsZMAY\nY4wxxpgiZcmAMZ8yHY+GWXf0KBq/OZzkKm/WcpqE1l9U0XDkKJp/UoPG+pfZ8O1hxN4OANB45nDW\nHT2q+2ftwXUk67PvP5fmB8v5+NBxLDupjtiK7AOQmoT6y4fx8cE7sOr7I0i5MSabPXxy5ig+PnQc\ny08dTWypL+f2LWH5UuGF5zy8PtNDx8bcZVMpePtN4dmnPLz0vId1Db0fnzdXWLF8aPevbp9Ryqoj\n6qg/bSSJlbmP84arq1k1tY71F9f2O84bnw3T8O3hGZ/beEUNG58MDynOdE1vell0fZBlt5QQ27Bp\n73/N436a5/R+f/VP+/nw6iCLrgvSMm/zzsG+Fv3fcB7fcX+eP/QztC8NZC2XjAn/PGNXHq6czFN7\n7Ef9SxU5tw9V24wyVkwdw5pTRxIf4Dg3XlXDisPGsO5Hw/of52fCrD2v5zgn13tYPmkcK6aOYcXU\nMay7qHaz4os+Hqbl2BG0fauW5Orc8XVcV0nLMSPYeHlVr/iiD4dp/14NG6dXktrQ0+zQFLRfWEN8\nTvbjMVgN91byzsRdeP+YHYh+MvD1YdnFI1k/o/exbJkZ5qMzx7DorDra5wa3SFyRx8I0HTOClrMG\nrseNv6yk6csjaPtp73qMPBym9YIa2q+pJNXo1GPLN4fRdMyI7p8Nh44iWb9tmnb3czpnc8c2eS2z\ndVkysJ0RkatE5N2032eKyFmD3Mddg31Ojn2dJyKrRaReRL7b57GDReQDEWkQkR+LyFQRiYnIBhFZ\nICJf3RIxbEbMW+z9b2nxj31s/EsFNfc1UH5xC62/rspatvPRUpJrvAx/th7/+BgdD5X1fvypMPE5\nJd2/1963juHP1zP8+Xqqb1uPb3wc76jBj6FGP/TTeHMVOz2+mpFXNtJwTfZGSPOD5cRX+dj19U8I\n7h2l6e5KABpvq6TyxHZ2m7WCiuPbabiuNuf2oWptgY8+FD4/NcU++6aY/27uS+OSjwXxwJFfTjF+\nL+W9eT3lP1worFoxtEQgtshP6+0VjHqonuqfNNF0fXXWsu1/LSOx2kfdC6sJ7BWj7f7y7sdSG4WW\nmyqpuri53/Mi/y6h42+lQ4qz1/7qhcZX/Ox0QYSRx8ZZ+7R/wOese9FH6zu9G0at87zEGoRdfxRh\nzDdirH3KTyoxtNia54dYcN0Yjn5jAZNuXM7bP9wpa9kPfj8Kj0/56vK5TPjZSv7z/Z1ybh+K2Ed+\nWm6rYPSMemoua6LplzVZy7bPKCOxysvYl1YR2DtG6329j3PT76uovqTnOMc+CFB2fDvjZq5i3MxV\nDP9t46DjSy72EbmzjPI71xP6YSudv82eAMWeCJOq91LxZAPe8XGiM5xzK/Z8kPi/Swj9pAVPbYrO\n3zv70AR0XldJYu6WSwQ6FgZY8/ta9n5uOTtMb+CTK0bkLL/q17U0Plbea1t0uZ8VPx/BmEvWM+yU\nVhafNxrVocWV+NhH551lVN69nvBFrXT8Jns9Rh8Pk1zjpepvDfjGx4k87NRj9O9B4m+UUHqZU48d\nbj1W3rOe6mcbqH62gYpbGvGOj+MdlRpawJvgab7Cd7kZpWD/aNcm8SS33U8hs2Rg+zRBRA7JdxAi\nMh74FTAVOBSYLiJ7pRX5CXCtqo6A7u6FOUAt8GOcP6k9YdtFvGWIyIUikr2VPgTRmSGCx3TgHZ4i\nsG+MVLMH7cx8MY6+EiR8Zjvih9DXNxJ9taeHK7XBw8Y7ywlMiWR87sY7yyk9p22zYmx7sZSKE9vx\njUwSmhgl2eQh1ZE5xvZ/lFJzTgsSgKozWml/yemljn0cILh/FIDwQRESa705tw9V/Rph7DglGILq\nGojFIJGjAVpeoUzYT/EHYMQoJdLpvvdW57mjxwyt9dD5cojwcR14R6Qo2S9GKkcddr4UovybbYgf\nyk5pp3NmqPuxllsqCX4+QmD3eK/naBSarq8m9MWOIcWZrn2hl4qJCfwVENohRbJDukd6Mok2CMlO\noXxC729JX6Uy+qQ4vnII7+w0alKZT9NNtvKpanY6fT3hujjDprQTbfSR2Jj566963w4m/2E5gaok\ndcc007kqkHP7UHS8HKL0uI34RiQp2T9Gsjn7ce54KUzFWc5xLj+1jc5XekZ0mm+uJHRYZ6/jHPsg\nQGDPeKZdbbL4a0EC0zrxDE/hmxBHW7Jfb+Izg5Sc5lxvSk7qIP66e71JCaGLW/GOTRI4upOUO1KY\neKsEz9gE3n1ynCSD1Px8GbVfayUwKknZpAiJDV6S2T43HwVINHupOa699/tY72Xc9AZK941SfUw7\nGvWQ2ji0Bm/8tSAlbj36J8RzXrdjM4MEz3Dr8eQO4q+59ahC+JJWvOOcekxmGPXovLuM0Nnt/bZv\nDXdyNlcyfZu8ltn6LBnYPrUC3853EMCxwPOq+pGqLgH+Dnwl7fFqYAWAqnZ3W6njWeCvwBnbMN4t\n5UJgqyQDqQYvvrQvfO/wJMk1mRvEybSynjIl1dxTru3GSkrPbsNT27+7ItnoIb7AT8lhm9cCS9R7\nKdmz5wveNyJJfHXm4fr4Wi8l452y3nIlucGJsWSPGC2PlJNqF5ofLif82c6c24cqEoGKyp7fS4LQ\nmaOdPHIU+N2O7/XrhGq3Q7esHCbsN/S+suRaL/49NvE4r/US2MOpQ0+5dk/DiC/z0f5QGYkVPhov\nrSG2oKenvuX2SsJf6sC/+9Aai+kSLUJwZE8S5CtX4s3ZayIwXBl1XJy+lRXeKYW/ytlP50rBG1J8\nZRl2MAgdqwJUTeg5oKHRMTZ+krkxX/flFgKVzudi7cuV1B7UnnP7UDjHrvdxTmQ5zom1XvxpxznZ\n5B7npT7aHiwnscLHuktqiS5w3ldsoZ/WOytYcfgY1pwxkviywU+pSzV48O7aE59nWIpUlqmDqXVe\nvLs5GbSUKdrsxBeY1om3Lom2C5F7y/B/wfnM+g6OEvzvAebjDVJsjY/QXtHu3/0jk8RWZh6hCu4e\nY8drG8DTO3EvmxSh8rAOUnFYe0cV4X0jeMuGltynGjx4d0urx+E56rHBi8+tR+e67dRjiVuPKbce\nA0f0vvalGj0k3g/g/3y03z63hkf4GrUMfrSp0NjIgMOSge3TX4ETRKR7boGI3JX2/5nulJxlIvKA\niKwRkevc6TrfdIt9UUQWudN4Jqc996ci8omILBeR49K2LxORI0TkFRG52928C7A8La5PgJ1E5AQR\nqccZLXjMnUI0PsP7eAfY093/QSIy143xdnFMFZF57vtZIyJXp8WTqfxObpw/cl/zXREZ5ZY/153O\nNAsYszn7EZHvue9rHPAf97Gwu58TRGSxu587RGSzPnuaAk9pzxCwBJVUW5ZdpQRPadqXmNvTHZ1d\nQqrJQ+j4zK3dyFNhQsd1IJvbok1K7xjDSqo1S4xJwVOWNqTtxlj59TZaHytj5bdHsvHlMNWnt+Xc\nPhj/me3hhed6/2gKfL60hqxXiW9COzmZhI8WCrvu7ryHza6zvvoe51Cu4wySdpw16QTRdk85vnEJ\nyk5px79HnIZvjyDZ6CG2yE/k9SAV57dudngr7wuw6Ppgrx9NgSfYE4cnoCQj2StkU+pq3fN+ag4b\n4hwhnHnY/oqeb2NvOEWsOXfjOBkV3ps+hr0uXrNJ2zdLSpC089+T6zgn6fN5diqw9e4KfOMSlJ/a\nTmCPOGvPc46zb2yCYTeuY9yrqwgf2UHTr7NPNcsZX/prBhVtz3Lgkr3PQ/o0ftovrCHx7xICRzqd\nDFvss9IrBsFbnlaf4RTJLNeegV5/1S+Hs3L6cEac3X+K3ebElV43znU7SwCp3PXY9v0a4rNLCBzV\nu7Mm+nSIkq8M4bo9SJ/uyUGmL7u16PZpPfA08F+bUPZZIAAMB67BmdIDTiN8AjANuBNn6tE04IvA\nXsBYYKaI7KCqXc2m3wDfB+a5vwfdWLrEgJCqPgk8KSIzgatUdSaAiIzuE1s7UCYiAeB+4GvAh8Df\ngBOBJuAzwEHAYmCOiDyHM9UoU/m5QB0wyv33KeA0EZmBM53pAJxr3HvA/TleN+N+VPV3wJ9EZBkw\nVVWXpb2X6cAPgOeAW4Cd3Zi7icj5wPkA5ZddT/ikM+nLU57q1VjQqPTr2ereX3mKVJvTswqgMUEj\n0H5TJVW/yd6j0/lcmOo/bH6Pj6cy2TvGiGTtdvBWOF/WnrDzjZeKOV8x9ZcPY8xtawlNjBJb7mPV\nd0ew05Ors24fjAOn9J9P+/58IR4XwKmrZFIQGbg38KMPhIpKZcTIQYUwIE9FpuOcuaxUOA3IrjpU\ntw5j7wWo+kEzoS84jYbIrCDRt0toe6Cc6iuakIGn9Gc19sz+Uzsa/u4jmTb1IRXftDrMpunfXjQJ\nVZOH3qUWqE4ST2v8JyMeJMvnpst714yhat8O6o5q2aTtm8NTkeqVKGtUsjbmus6JnuPsbI++F6D6\nwmbCR3QCnXTOChJ5q4TqH/bEV35qOy03V2bYa25Snurd+I+RtRXYVVbSrjfpyv/cSPTJEBt/UUn5\nzRsGHcum8FYmSbb01GcqIpvdah3383XUntLCR98Yy4R/LR3S6IBUpNC0xr9GIVt3kJQ7ZbPVY+Ud\njUSeCLHxmkoqbu2px9jfQ5T9buvU6/bMM/S+hu2CjQxsv/6X7FOF0q8ub+A0urv+7Ton7lPViKo+\nDuwiIhXAl4ADcRqxrwKlOI3hLjeo6j9Vtau7tgMnIehS4m7bVKVuTOOBnYDngWXAJGBvt8y7qjpH\nVVtwGtpTBiivOAlICngbqHTf02xVXe424F9yyw52P7n8E7gYOA+YrqqL+xZQ1dtVdbKqTs6UCAD4\n94oTfy/glof4h368wzMvFvPvHesum1zlRYJK/IMAyXovG77l3Dko8mKY5ktqifzDmWeeWOpDArpZ\nC4e7BPeJ0enegUMVou8H8I3MfMUtmRAl8o6ziDm2wtfdsxx5J0hgN6e1E9gxQaLeRyoiWbcPVVUV\nNLnfo6rQ0gLBAW4i0rAW1qwSJuw3xNWFGQT2jhGb13OcYwsDeEdkPiaBvWPE3nXKJlZ68QTdUYrS\n3sdRShSNCPGP/Ky/cBirjqij7e5ymm6opvXu8oz7HozgGKVzhac75uhqwbeZu42sEda/4mf01+JZ\nG02DUXPARtb/u6w7tqa5pYTqsg/9rH6+kk8ereHAPy7bpO2bK/CZGLF5Jd1xRRcG8I7Mcpw/EyPq\nnhPxlc7nGcBTlsI3uufzJQHFU6a0P9WzODzZ4AUPg14I690zTmJ+z3mY/NCPZ3jm+Lx7xUl0XW9W\ne5ES58Xis0pQd+aK/4gIqS14B7C+SveL0P52qDvejvlBAqMH19rrWFBCZJmTKYf3iuEfmSC2agiZ\nM5nrUbLUoy+tbHJ1z3GOzSpB3cGAwBcjJNPqMbnUB362ycJhs32ykYHtlKr+S0SSwETgrj4Pj0n7\nf7LPv9276PN/D04Sca2q/grAXSSbPnF2dp99LMFpnHfZEXhzE98CwL7AAvd1P1bVvdzXDQJenEZ8\nekvQA6RylB8O1KtqV0LS9R6F3u83lbZ9MPvJSlUvEJFDcUZW3haRI1R1wSbVQprAoRHafl9JZHKU\nxGI/nspU1oZ78KhOWq+rwlOVouOhMkoO7ySwf4wRr/RMb2i5qorQcR0EJjkN7OisIIHJQ5tzWnp4\nB+uuqyE8pZPoogCeqhT+0ZljrDh2I2uvrMVbk6LprgrKvuRUqX9snKa7K6k4ZiNtL4XxlKXwBDXr\n9qEaPlJZMN/DsOFKW6sQCEAoxx03N7bDO297OPCQFP4tdzOUbsHPRWj+TRUlB0WJf+zHU5XCl+U4\nh6d10DS9Gk91ivb7ygi5c7JLJkXZ+HQp/j2biS/yE1sQoPb6RkrTpoe13FqBry5B6QlDX0hcunuS\nhmf9tO2SJLrWgzdM99z/wUh2wMp7A4w6PkagZsskWnVHNzP30h0YObWVlgUhSmoTlI7LvHC1bXEJ\ns8/ZhcMf/4hAVXLA7UMR+lwnTb+uInhwhNgiP97KZNbjXDqtg8ZravBWp2i9r9wdCXCOc/tTpVTv\n2Uz8I+c4B/aJUn/mKHyjEwTGx2i5rZLQ1M5BTyHxHxKl8w8VxCbFSC3xIZUpPCMzNzgDX4rQcUMl\nnuoU0YdL8btrjuKvBEnMCxD6Thvxl4N499py61T6qvxCByuuHkHFZzvo/DCArzpJoG6QycDCAI0P\nV7L7vauILvcTX+ejZOehLXIOHBql4w8V+CfFSLr16M1Wj0dF2Hh9JVKVIvJwKQG3HmMvB0m8EyB8\nQRuxl4L49k5bLP5GCf4hXreLlSdpE57ARga2d7cCFUANzjx2ROQYYNdNEVQINwAAC6hJREFUeO5p\nIlLirgtYparNwIvAKSJSISJ1OCMEuRbKPg0cJSLjRWQX4CjgmU0JXESOAk4G7gE+AMIi8nl3rv29\nwLlu0X1FZLKbmEzDGeHIVT5T62IOMEVExorIDjgNdjZjP13WAzu66wtq3PezAFgFXAssxEl0Bs1T\nplRev4GOB8qIzwlQeU0TsXkBmi7sf4vNwAExSs9sp+23lUhYKT1v4Pn18TkB/BOG9sXnLVPq/thA\n052VdL4ZpO436+icU8LK8/rPpQkfGKH6nBYaflGDpzTFsO81ATDy2vW0/b2UZSfU0fpoGaNuWJdz\n+1D5/TD5oBRLPvbQuF6YOMn5ot7QCG++0f8yuWypEI/DW2nrDyJDvONNOk+ZUvvbRtruLSf6dgm1\n1zUSfSfAuu8N61c2OClK+TfbaL6hCilVKv7HWQtQcW4rqVYPq4+sY/0Fw6i+oglPxZYfxejiDcKY\n02Js+JePjmUeRn/dOY86PvGw4p5Nz5ha3vGSaBPqn+pZl9C5cmhf2P6KFJ974GM+uGkUDa+Xc8hd\ni1n3RhmvnrhHv7KL/nck8RYvr31tdx7fcX8e33F/Otf4s24fCk+ZMvy362m9p5zo20GGXd9I5J0A\nDRf0/7sQwclRKs5qZcOvqvGUKpXfcaYBVZ7XSqrNw6ov1tFwwXBqrtyAt1Kpnd5I41U1rDqmDk1C\nzU+bBh2flCql1zYRfaiUxNwA4Z83k3jPT/tF/dcf+CbGKDm93bnlZThF8FtOP1Hwu60kP/TTcswI\n4i+GCF8y9OlV2XjLU+x6+2rW/l81bbPD7PLHNbS/FWTRN+sGfrKr9uQ2QuOjvDtlZxZfMJpdbl6D\nZ4gJv5QqZb9sIvJgKfG5Acqubib+rp+2H/WvR//EGKEz2un4XQUSThE6x6nH8PdaSXzop2naCGIv\nhghf2lOPibkBfPtsvSTLbP9Eh3oDXVNQROQqIKiqPxGRcmA1cClwOs7yzPnAfsCVOCMGU4GrgJnu\nLqa6/9bgNFhjwFmqOsvd/5U4U12SwOWq+oC7fRn958kjIufizJf34EyruTXtsZn0XjMwFfgHzmjD\ncuBSVf2H+9gUnKlPI3GSknNwFiDfCGwA9gFuUdVrcpSvA2aq6k5pdYWqXiXO30C4AqjHuRvTnap6\n12D34/5+DHAzTiL2PVV9UEROBa7Hmfr0b5w1BllvRzLqrVUF/8GsqtxytwTcGna/ZId8hzCgeVcP\nbr3Dtlb6t/7JR6GZePF7+Q4hp1mLRuU7hAG1tA0tqdkWdtth8251vK0sWTn0qXZbW+OUTU+K8kZ1\nm3bV/2DcwCP7W8pNKwp33bUlA+ZTy00erlLVqXkOZYuzZGDoLBkYOksGhs6SgS3DkoGhs2SgP0sG\nHLZmwBhjjDHGFJ1Cv///tmLJgPnUcqcXTc1zGMYYY4wxn1qWDBhjjDHGmKJjIwMOu5uQMcYYY4wx\nRcpGBowxxhhjTNGxkQGHjQwYY4wxxhhTpCwZMMYYY4wxpkjZNCFjjDHGGFN0bJqQw0YGjDHGGGOM\nKVI2MmCMMcYYY4qOjQw4bGTAGGOMMcaYImUjA8YYY4wxpuh4EvmOoDDYyIAxxhhjjDFFykYGjDHG\nGGNM0bE1Aw4bGTDGGGOMMaZI2ciAMcYYY4wpOjYy4LCRAWOMMcYYY4qUjQwYY4wxxpiiYyMDDhsZ\nMMYYY4wxpkiJquY7BmPMNiAi56vq7fmOI5tCjw8KP8ZCjw8sxi2h0OODwo+x0OMDi9FsOzYyYEzx\nOD/fAQyg0OODwo+x0OMDi3FLKPT4oPBjLPT4wGI024glA8YYY4wxxhQpSwaMMcYYY4wpUpYMGFM8\nCn1eZ6HHB4UfY6HHBxbjllDo8UHhx1jo8YHFaLYRW0BsjDHGGGNMkbKRAWOMMcYYY4qUJQPGFAkR\n+R8RqReRt0Rk53zHk42InCEid+Y7jkxE5Cci0iIizSJyUb7jyUREDhSRJ0XkeRGZlu94shGRs0Xk\nrnzHkYmILHQ/K/UisjLf8WQjIlUi8omI7JLvWNKJyKVp9VcvIu0i8rN8x9WXiPxMRFa6P2fnO550\nIhIQkdvc2N4Tkc/nO6Z0ma7TInKiez4uFJED8hWbGTxLBowpAiIyAbgSOAD4f8Cf8htRZiJyLHAz\nIPmOpS8ROQQ4DZgIHAr8XETG5Teq3kSkErgf+D1wHXC3iFTnN6r+RGQ48Ot8x5GJiIQBVdVR7s/Y\nfMeUwy+AB1V1Sb4DSaeqv0qrv1HAO8CT+Y4rnYjsAxwO7AgcAvxKREbmN6pezgdKgZ2As4EZIlKe\n14hcma7TIjIC+DMwDTgWW0vwqWLJgDHF4UTgHlVdrapvAMNEpDTfQWVwNjA930Fk0QGcrqpLVPV9\nYBlQl9+Q+hkNXKGqr6jqTGAFUIiN2d8BL+Q7iCz2A97LdxADEZGJwEnAtfmOJRcROQJYp6rz8h1L\nH3sBb6lqUlVXAMuBUXmOKd2pwK2qmlDVt4D1wPg8x9Ql03X6aOAFVV2gqouBT0Rkj20fmtkclgwY\ns50Rkb3codoyEdlTRJbj9C69m1ZsNU6PWF5kitHt9foa0JivuHLFByxR1QXu43XAOGB+gcW4SlVn\niIhXRE4G/MD7hRKfiJSLyJE4CUreew6z1OFBwOdEZLWILBaR4wstRvezchPQCtwnIt8RkbyMpuWI\nr8uFOCNVeZPlOH8EHC8io90pOMOAhQUUX5kbE2597oiTEOQ1rhzX6bH0/o5ZDuy2rWI1Q+PLdwDG\nmC1LVReKyIPARThTWi4FvoTTcOiyEajKQ3hA5hhVtQ0gT22aXnLF57oWuE1VN+YlQAaM8QdujBer\narJQ4gMSwI04Pdpj8hFXuiwxBnDq8j4RmQI8KSLPq2q0gGKcgDNV7cc4oz/XAwr8byHEl/ZZHgvs\noqqvbuu4BooRp+G6AngGKAd+o6qxAoqvCviTiOwJfBX4QFWX5TuuHNdpL9CU9ntev2PM4FgyYMz2\n6RpgLrBCVR8SkUn0vjCHgFReIuvRK8Y8x5JJxvjEWZT7OWD/fAWWJmOMqnqjiMwA/u02ZD8uhPhE\n5HrgXlVdJCJ5TwZcWc9DVZ3t9tLuB7yZj+Bcfevxh8DfVPV3AO6Uv1PIQzKQKb607acDM/ITUj99\n6/BsYKmqHiUiHuB5EXlTVf9TCPEBiMha4LM4a71OK5S4smii8L5jzCayaULGbJ9COD2cFSLiBd7C\nWSSHO53gAGBV/sID+sdYaPrF5y4Y/jNwRj5HBdL0ilFEdhORfQFUdSXwH/I7z7hvHR4HXCQi9cBj\nwKki8lge44P+dfhNEQmkPT6G/Ddq+tZjK/BJ2uMRoDkfgbmyfZZPBp7IT0j99I1xCu4UOlVN4Sxy\nnpy/8PrXoao+DrwKLMD5vBREXFl0f8e4JgMFeycu05slA8Zsn27EWaS5APg+8BzwVRE5CeeuQo3u\norl86htjoekVn4iUAE8Bv1bVfPYSp+tbh3XAw+LccnI0cCBOr15BxKeqn1HVke4dZk4CHlbVk/IY\nH/Svw8NwEpawiHwXiJH/BcV9Y/wn8GURGS4iIeBcnEZjocSHiNQCO6vqu7meuA31jXEpTjK6n4hM\nxel5z9saILJfDy8Dfqr5+wuxm3qdfhPYXUTOFZHzcdYLFMp10gzApgkZs50RkS/izO/8FlALvA08\nirPo69dAFDgjbwGSOUYReVRVP8n9zG0jSx1uwJkaNFpEfuIW/a6qPlpAMR4K3IOzEHIjcKGqri6U\n+ArpGEPOOrwX+CkwBzg+X+sFcsT4KM6akH+52/4G/KVQ4hORR4FJFEhjMEsdfhYnxteAOM6de14v\nlPjcOtwHiKnqc4UUV6bPsKqqiJwA/AGoAE7O1xoMM3iSv2TTGGOMMcYYk082TcgYY4wxxpgiZcmA\nMcYYY4wxRcqSAWOMMcYYY4qUJQPGGGOMMcYUKUsGjDHGGGOMKVKWDBhjjDHGGFOkLBkwxhhjjDGm\nSFkyYIwxxhhjTJH6/5HEKMOF6Ap2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146a18d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = train.corr() # 计算各变量的相关性系数\n",
    "xticks = ['x0','x1','x2','x3','x4','x5','x6','x7','x8','x9','x10'] # x轴标签\n",
    "yticks = list(corr.index) # y轴标签\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "sns.heatmap(corr, annot=True, cmap='rainbow', ax=ax,\n",
    "            annot_kws={'size': 12, 'weight': 'bold', 'color': 'blue'}) # 绘制相关性系数热力图\n",
    "ax.set_xticklabels(xticks, rotation=0, fontsize=12)\n",
    "ax.set_yticklabels(yticks, rotation=0, fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 上图可以看出变量之间的相关性都较小，但是 NumberOfOpenCreditLinesAndLoans 和 NumberRealEstateLoansOrLines 相对来说较大为0.43"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAHWCAYAAABucBCJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3XmYVNWd//H3N0T9KQSFCHZEpWP0\n54ICCioYIW1UFHHBqEzUGIIYFCbOggaNtjoo/kBNMjMyGuMAJjrOiDFEgwuKI63ggNguuGZkRHEn\nRFoEwgSE8/ujm5all+qlurov79fz1NO3bp1763vqgf70PffUvZFSQpIktX1fKnQBkiSpeRjqkiRl\nhKEuSVJGGOqSJGWEoS5JUkYY6pIkZYShLklSRhjqkiRlhKEuSVJGfLnQBTTU7rvvnoqLiwtdhiRJ\nLeb555//U0qpS33t2lyoFxcXU15eXugyJElqMRGxNJd2Dr9LkpQRhrokSRlhqEuSlBGGuiRJGWGo\nS5KUEYa6JEkZYahLkpQRhrokSRmx3YX6smXLGDBgQK2vr1+/nlNPPZVvfvObTJs2rdZ17777LiUl\nJXz7299m1KhRpJS49tprKSkpoaSkhAMPPJCJEye2SJ8kSYLtLNQrKioYPnw4a9asqbXN5MmT6dOn\nD8888wz3338/q1atqnHdL3/5S37xi1/w5JNP8t577/HKK68wfvx4ysrKKCsr45BDDuH73/9+C/ZO\nkrS9265CvV27dkyfPp2OHTvW2qasrIxhw4YBMHDgQMrLy2tcd8MNN3DQQQcB8Mknn7D77rtX7+O5\n555jr732olu3bnnsjSRJW9quQr1jx47suuuudbZZs2ZNdRh37tyZZcuW1bhuk+nTp9OjRw/23HPP\n6nX//M//zCWXXJKHHkiSVLvtKtRz0aFDB9auXQvA6tWr2bhxY43rAJYsWcJPf/pT/umf/ql6+08/\n/ZQ//vGPfOMb32j54iVJ2zVDfSt9+vRh3rx5ACxatIji4uIa11VUVHDOOecwbdq0LY7+H3zwQU4+\n+eSC1C5J2r61uVuvNqcnn3yS119/nR/96EfV64YPH87JJ5/M3Llzef311znqqKPo1q3bNuuuvPJK\n3n333eph9vHjx/Otb32Lxx57jMsuu6xQXZIkbccipVToGhqkb9++Kd/3U//www+ZN28eJ554YvVR\neE3rJElqCRHxfEqpb73tDHVJklq3XEPdc+qSJGVE3kM9IvaIiLl1vL5DRMyMiGci4oJ81yNJUlbl\ndaJcRHQCfg20r6PZJcDzKaV/iIhHIuI3KaVV+axra8VXPNzkfbwzaUgzVCJJUuPl+0h9A/BXwGd1\ntCkB7qtafhrY5pxBRIyKiPKIKF++fHmzFylJUhbkNdRTSp+llFbW06w98EHV8gpgjxr2c0dKqW9K\nqW+XLl2au0xJkjKhNUyUWw3sXLXcgdZRkyRJbU5rCNDngWOqlnsB7xSuFEmS2q4WvaJcRHwbODil\n9C+brf418EhEDAAOBp5tyZokScqKFjlSTymVVP18cqtAJ6W0FDgBeAY4PqW0oSVqkiQpa1rFtd9T\nSh/yxQx4SZLUCK3hnLokSWoGhrokSRlhqEuSlBGGuiRJGWGoS5KUEYa6JEkZYahLkpQRhrokSRlh\nqEuSlBGGuiRJGWGoS5KUEYa6JEkZYahLkpQRhrokSRlhqEuSlBGGuiRJGWGoS5KUEYa6JEkZYahL\nkpQRhrokSRlhqEuSlBGGuiRJGWGoS5KUEYa6JEkZYahLkpQRhrokSRlhqEuSlBGGuiRJGWGoS5KU\nEYa6JEkZYahLkpQRhrokSRlhqEuSlBGGuiRJGWGoS5KUEYa6JEkZYahLkpQRhrokSRlhqEuSlBGG\nuiRJGWGoS5KUEYa6JEkZYahLkpQRhrokSRlhqEuSlBGGuiRJGWGoS5KUEYa6JEkZYahLkpQRhrok\nSRlhqEuSlBGGuiRJGWGoS5KUEYa6JEkZYahLkpQRhrokSRlhqEuSlBGGuiRJGWGoS5KUEYa6JEkZ\nYahLkpQRhrokSRlhqEuSlBGGuqTt0siRI+nfvz8TJkyo8fWKigpOPvlk+vbty0UXXVTvdsuWLeOw\nww4D4Be/+AUlJSWUlJTQu3fvLbaX8slQl7TdmTFjBhs2bGD+/PksWbKExYsXb9Pm7rvv5rzzzqO8\nvJxVq1ZRXl5e53aXXXYZa9euBWD06NGUlZVRVlbGgAED+OEPf9hifdP2zVCXtN0pKytj2LBhAAwa\nNIh58+Zt0+arX/0qr776Kp9++invvfcee++9d63bPfnkk7Rv356ioqIt9vHBBx+wbNky+vbtm+ce\nSZUMdUnbnTVr1tCtWzcAOnfuzLJly7Zpc8wxx7B06VJuueUWDjroIDp37lzjduvWreP6669n0qRJ\n2+zj1ltvZfTo0fntjLQZQ13SdqdDhw7VQ+WrV69m48aN27QZP348t99+O9dccw0HHnggd955Z43b\nTZo0iTFjxrDbbrttsf3GjRuZM2cOJSUlee+PtImhLmm706dPn+qh80WLFlFcXLxNm4qKCl555RU2\nbNjAs88+S0TUuN0TTzzBrbfeSklJCS+99BIXXnghAHPnzuWoo44iIlqsX9KXC12AJLW0oUOHMmDA\nAD788EMeffRR7r33XkpLS7eY0f6Tn/yEESNGsHTpUvr3788555zDxo0bt9huwYIFnHvuudXblJSU\nMGXKFAAee+wxBg4c2OJ90/YtUkqFrqFB+vbtm8rLy5t1n8VXPNzkfbwzaUgzVCKppVRUVDB79mwG\nDhy4zQS3fGwnNUVEPJ9SqnfGpUfqkrZLnTp1qp7J3hLbSS3Bc+qSJGVE3o/UI2IqcDDwcEppm0s3\nRUQn4B6gK/B8SslLL0kqCE/Fqa3L65F6RHwHaJdS6g/sGxH719DsfOCeqnMFX4kIr9IgSVIj5Hv4\nvQS4r2r5ceCYGtp8AhwSEbsBewPvbd0gIkZFRHlElC9fvjxftUqS1KblO9TbAx9ULa8A9qihzTyg\nO/A3wBtV7baQUrojpdQ3pdS3S5cu+apVkqQ2Ld+hvhrYuWq5Qy3vdy1wcUrpOuAPwIg81yRJUibl\nO9Sf54sh917AOzW06QQcGhHtgKOAtvXFeUmSWol8h/oDwPkR8XNgGPBaRGw9A34icAewEugM/Eee\na5IkKZPy+pW2lNJnEVECnADclFL6GFi0VZuFQI981iFJ0vYg799TTylV8MUMeEmSlCdeUU6SpIww\n1CVJyghDXZKkjDDUJUnKCENdkqSMMNQlScoIQ12SpIww1CVJyghDXZKkjDDUJUnKCENdkqSMMNQl\nScoIQ12SpIww1CVJyghDXZKkjDDUJUnKCENdkqSMMNQlScoIQ12SpIww1CVJyghDXZKkjDDUJUnK\nCENdkqSMMNQlScoIQ12SpIww1NUsRo4cSf/+/ZkwYUKd7caMGcPMmTMBePvttxkyZAgDBgzg0ksv\nrbXdypUrGTx4MIMGDeKMM85g3bp1+emEJLVxhrqabMaMGWzYsIH58+ezZMkSFi9eXGO7uXPn8vHH\nH3PqqacCcPnll3P11Vczd+5c3n//fcrKympsd8899zB27Fgef/xxioqKmDVrVov0S5LaGkNdTVZW\nVsawYcMAGDRoEPPmzdumzfr16/nhD39IcXExDz74IABvvvkmhx9+OABdu3Zl5cqVNbYbM2YMJ5xw\nAgDLly+na9euLdEtSWpzDHU12Zo1a+jWrRsAnTt3ZtmyZdu0ueuuuzj44IMZN24cCxcuZPLkyZx1\n1lmMHz+emTNnMmvWLI477rga220yf/58Kioq6NevX4v1TZLaEkNdTdahQwfWrl0LwOrVq9m4ceM2\nbV588UVGjRpFUVER3/ve95gzZw6lpaUMHjyYKVOmMHz4cDp06FBjO4AVK1ZwySWXMG3atBbtmyS1\nJYa6mqxPnz7VQ+6LFi2iuLh4mzb77bcfS5YsAaC8vJzu3bsD0Lt3b959913Gjh1ba7t169Zx9tln\nM3HixOrtJEnbMtTVZEOHDuXuu+9m7Nix3HffffTo0YPS0tIt2owcOZI5c+YwcOBAbrvtNi677DIA\nbr75ZsaOHcsuu+xSa7upU6fywgsvcMMNN1BSUsL06dNbvI+S1BZESqnQNTRI3759U3l5ebPus/iK\nh5u8j3cmDWmGStquiooKZs+ezcCBAykqKip0OVKj+LtArVVEPJ9S6ltfuy+3RDHKvk6dOlXPgJck\nFYbD75IkZYShLklSRjj8rrzx/KQktSyP1CVJyghDXZKkjDDUJUnKCENdkqSMMNQlScoIQ12SpIww\n1CVJyghDXZKkjDDUJUnKCENdkqSMqPcysRGxA/B1oBPwCfB2SmlDvguTJEkNU2uoR8TewHVUBvpr\nwKfA7kCPiHgZuDaltLxFqpQkSfWqMdQjYhhwMfDjlNLzNbz+LeB3EXF1SmlOnmuUJEk5qO2c+h7A\nSTUFOkBK6SlgMLB/vgqTJEkNU2Oop5Qmp5TWbb4uInpFxLkR8X8i4tSU0qqU0h0tU6YkSapPTrPf\nI+Ia4B5gMhDAFRFxUz4LkyRJDZPrV9pGA/2AdSmltcCxwLl5q0qSJDVYrqH+KbAbkKqeFwOr81GQ\nJElqnHq/p17lUqAM6BQRDwOHARfmqyhJktRwOYV6SumRiFhI5RB8AAv8jrokSa1LTqEeEd/fatXg\niCCldFceapIkSY2Q6/D7sVU/A+gGfAv4HWCoS5LUSuQ6/D5i8+cRcSjw47xUJEmSGqVRd2lLKb0C\n7NvMtUiSpCbI9Zz6HL74OhvAXsArealIkiQ1Sq7n1P9hq+efpZRebOZaJElSE+R6Tv2pfBciSZKa\nplHn1CVJUutT65F61U1c6pRSuq55y5EkSY1V1/B7tFgVkiSpyWoN9ZTS+JYsRJIkNY3n1CVJyohG\nh3pE7NechUiSpKbJKdQj4nsR8VFEbNj0AJ7Pc22SJKkBcj1SnwAcDjwFfB34EfCrPNUkSZIaIddQ\n/zLwKTAH+CbwS2BYvoqSJEkNl+tlYqcCM4G/p/KWq6cAy/NVlCRJaricjtRTStcCI6ruzvYD4AXg\n1Fy2jYipETE/IkrraXdbROS0T0mStK1aQz0i/iUizoiITgAppfeqfs5LKf0spbS0vp1HxHeAdiml\n/sC+EbF/Le0GAEUppZmN6oUkSarzSP0zKofb34+I8oi4MSIGRcTODdh/CXBf1fLjwDFbN4iIHYB/\nBd6JiNNr2klEjKqqoXz5ckf9JUmqSa2hnlK6MqU0EOgE/B2VE+X+DlgaEU9FxLU57L898EHV8gpg\njxrafB94HbgJODIiLqmhljtSSn1TSn27dOmSw9tKkrT9qfecekppHbCIyvPoLwJvAT2B3jnsfzWw\n6ci+Qy3vdxhwR0rpY+DfgGNz2K8kSdpKXXdpOxMYAAwE9gMWUPmVtr8FylNKG3PY//NUDrkvAHoB\n/11Dm/8B9q1a7gvUe65ekiRtq66vtF0K/GfVz2eqjtgb6gFgbkTsCQwGvhsRE1JKm8+EnwpMi4jv\nAjsAZzXifSRJ2u7VdZe2o5u685TSZxFRApwA3FQ1xL5oqzargLOb+l6SJG3vcr34TKOllCr4Yga8\nJEnKE2+9KklSRhjqkiRlhKEuSWo2I0eOpH///kyYMKHG1z///HP22WcfSkpKKCkp4ZVXXgGgd+/e\n1etmz54NwCWXXMJxxx3H0UcfzTvvvAPASSedVN3u7rvvbpE+tSV5P6cuSdo+zJgxgw0bNjB//nwu\nuOACFi9ezP77b3l18JdffplzzjmHG2+8sXrdJ598woEHHsi9995bve6FF16ga9euTJ48menTp3PL\nLbfws5/9jJQSZWVlLdWlNifnI/WI6BURAzd/5LMwSVLbUlZWxrBhlXflHjRoEPPmzdumzYIFC3jo\noYc48sgjGTlyJJ9//jnPPvssCxcu5Oijj2bo0KGsWrWKww8/nKuvvhqA5cuX07VrV9544w0WLVrE\nMcccw3HHHcdHH33Uov1rC3IK9Yj4DTALuA4YX/X4h/yVJUlqa9asWUO3bt0A6Ny5M8uWLdumzRFH\nHMETTzzBwoULWb9+PY888gj77rsvjz32GP/1X/9Fz549ufPOO6vbV1RUcMcddzBq1Cg6duzIo48+\nyrx58zjvvPO4+eabW6xvbUWuw+/HAvuklP6cz2IkSW1Xhw4dWLt2LQCrV69m48ZtLzzas2dPdtpp\nJwD69u3L4sWLOemkk9hhhx2q1206p55SYuTIkUyYMIHOnTuz6667UlRUVN3ud7/7XUt0q03Jdfh9\nFpV3XJMkqUZ9+vSpHnJftGgRxcXF27Q5//zzWbRoERs2bOCBBx6gV69eXHXVVcycWXnn7fvvv59e\nvXoBcOWVV3LooYdy2mmnAXD77bdz2223bdNOX2jIRLn7I6Kcyhu6JICU0gV5qUqS1OYMHTqUAQMG\n8OGHH/Loo49y7733UlpausVM+GuuuYZzzz2XlBKnnXYaxx9/PD169GDo0KFceeWV9O/fn+HDh/PG\nG29w0003ccwxx1BSUkL//v0pLS3l9NNP51//9V854IADmDp1agF72zrlGuqzqx6SJNWoY8eOlJWV\nMXv2bMaNG0dRUdE2R9OHHHIIL7/88hbrvva1r/Hss89use6ggw5iw4YN27zHE0880fyFZ0hOoZ5S\n+nVE9ABOpPIo/fGU0mt5rUyS1OZ06tSpega8Wl6us9+HA48AewP7AA9FxPn5LEySJDVMrsPv1wL9\nUkofAUTETcAzgJfzkSSplWjIRLnIWxWSpMwqvuLhJu/jnUlDmqGS7Ms11K8Bno2ImVSeUz8F+Ene\nqpIkSQ2W60S5f4uI54ATqlbdklL67/yVJUmSGirn4feqEDfIJUlqpbz1qiRJGVHrkXpEjE0p/bxq\n+Zqa2qSUrstXYZIkqWHqGn6PWpYlSVIrVGuop5R+ttny+JYpR5IkNZbn1CVJyohGhXpEdIyIbzZ3\nMZIkqfFyvfb7K1ut+jPg3eklSWpFcj1S332r53sCf2nmWiRJUhPUGeoRcW1EbAS6RsSGqsdGoBy4\nukUqlCRJOakz1FNK41NKXwL+mFJqV/X4Ukqpa0rpVy1ToiRJykWuw++/zmsVkiSpyXK9ocsVEbEL\nW55b/78ppSfyU5YkSWqonEI9In4M/BjoBLwH7AW8DRyQv9IkSVJD5Dr8/jdAd2AWcBJwFvBcvoqS\nJEkNl2uobwQ6AnOBY4CHgEH5KkqSJDVcrvdTnwjcDwwHngZGAG/mqyhJktRwuU6Uuz0ipqWU1kXE\nQKA34CQ5SZJakVwnyt0OHB8Rm4brA0jAvvkqTJIkNUyuw++DgKHAyjzWIkmSmiDXUL8fmA4sBDZs\ntv6CZq9IkiQ1Sq6hfi7w/4A1eaxFkiQ1Qa5faXuRyslxX6LyXPqmhyRJaiVyPVL/E7ADMHCzdQm4\nq9krkiRJjZLrV9pG5LsQSZLUNDkNv0fEf0fEbvkuRpIkNV6u59TnAWfksxBJktQ0uZ5Tfw64NiKO\nABZsWplS8py6JEmtRK6hfhQwB9gZOLZqnRPlJElqRZwoJ0lSRuR6pE5EnAScTOUR+iMppcfyVpUk\nSWqwXGe//wSYBCypekyMiMvzWZgkSWqYXI/ULwYOSSmtAoiIO4FXgBvzVZgkSWqYXL/S9mdgn82e\n71W1TpIktRK5Hqn/LfBERLxY9bwXMDw/JUmSpMbIdfb74xHRAzi6atV/pZRW5K8sSZLUULUOv0fE\npRHRqWp5/5TSipTSQ1UPA12SpFamrnPqP04pVVQtP9USxUiSpMara/i9IiLOBd4HdoqIAUBs3iCl\n9HQ+i5MkSbmrK9SHA1cAnYD2wHVbvZ6Ab+epLkmS1EC1hnpKaSHwHYCI+CildGxtbSVJUuHl+j31\ni/JahSRJarJcv6f+WEScDuy6+UpvvSpJUuuRc6hTGeiLqDyXDt56VZKkViXXUO8BFKWUNuSzGEmS\n1Hi5nlP/NTAqn4VIkqSmyfVIvQ/wo6rbrb5N1RB8SsmvtEmS1ErkGur/kM8iJElS0+V6QxcvEytJ\nUiuX6zl1SZLUytV6pB4R/6++jVNKVzZvOZIkqbHqGn7/S4tVIUmSmqyua7+Pb8lCJElS03hOXZKk\njKgx1CPiRxFR58z4iGgfERfmpyxJktRQtR2p/xGYFRG9a3oxIr4JPAK8la/CJElSw9R4NJ5Sui8i\n5gPXRcQ+wBtABbA7cBDwOnB2SumPLVapJEmqU10T5d4DRkTEjsC+QCfgE2BJSunzXN8gIqYCBwMP\np5Qm1NFuD2BWSumwXPctSZK+UO8V5VJK64A/NGbnEfEdoF1KqX9ETIuI/VNKi2tp/lNg58a8jyRJ\nyv/s9xLgvqrlx4FjamoUEd8G1gAf57keSZIyq9ZQj4h/iYgzIqJTE/bfHvigankFsEcN77MjcDVw\nRR21jIqI8ogoX758eRPKkSQpu+o6Uv8M+Hvg/apAvTEiBkVEQ4bIV/PFkHqHWt7vCuC2lNKnte0k\npXRHSqlvSqlvly5dGvD2kiRtP2oN9ZTSlSmlgVROkPs74NOqn0sj4qmIuDaH/T/PF0PuvYB3amhz\nPPDXEVEG9I6IKbmXL0mSNslpolxELKJyKL0DlSHfk8rh9Po8AMyNiD2BwcB3I2JCSql0s/0P3LQc\nEWUpJS9oI0lSI9R1l7YzgQHAQGA/YAEwB/hboDyltLG+naeUPouIEuAE4KaU0sfAojralzSkeEmS\n9IW6jtQvBf6z6uczVV9ta7CUUgVfzICXJEl5UtfFZ45uyUIkSVLTeJc2SZIyoq5z6qPq2zildEfz\nliNJkhqrrnPq/evZNgGGuiRJrURd59RHtGQhkiSpaTynLklSRhjqkiRlhKEuSVJGGOqSJGWEoS5J\nUkYY6pIkZYShLklSRhjqkiRlhKEuSVJGGOqSJGWEoS5JUkYY6pIkZYShLklSRhjqkiRlhKEuSVJG\nGOqSJGWEoS5JUkYY6pIkZYShLklSRhjqkiRlhKEuSVJGGOqSJGWEoS5JUkYY6pIkZYShLklSRhjq\nkiRlhKEuSVJGGOqSJGWEoS5JUkYY6pIkZYShLklSRhjqkiRlhKEuSVJGGOqSJGWEoS5JUkYY6pIk\nZYShLklSRhjqkiRlhKEuSVJGGOqSJGWEoS5JUkYY6pIkZYSh3oqNHDmS/v37M2HChBpfX7lyJYMH\nD2bQoEGcccYZrFu3rvq1ZcuWcdhhh23RfvN1b7/9NkOGDGHAgAFceuml+euEJKnFGOqt1IwZM9iw\nYQPz589nyZIlLF68eJs299xzD2PHjuXxxx+nqKiIWbNmVb922WWXsXbt2i3ab77u8ssv5+qrr2bu\n3Lm8//77lJWV5bU/kqT8+3KhC1DNysrKGDZsGACDBg1i3rx57L///lu0GTNmTPXy8uXL6dq1KwBP\nPvkk7du3p6ioqPr1rde9+eabHH744QB07dqVlStX5rU/kqT880i9lVqzZg3dunUDoHPnzixbtqzW\ntvPnz6eiooJ+/fqxbt06rr/+eiZNmlT9ek3rzjrrLMaPH8/MmTOZNWsWxx13XP46I0lqER6pt1Id\nOnSoHipfvXo1GzdurLHdihUruOSSS/jtb38LwKRJkxgzZgy77bZbdZua1pWWljJv3jxuvvlmhg8f\nTocOHfLYG0lSS/BIvZXq06cP8+bNA2DRokUUFxdv02bdunWcffbZTJw4ke7duwPwxBNPcOutt1JS\nUsJLL73EhRdeWOM6gN69e/Puu+8yduzYFuuXJCl/PFJvpYYOHcqAAQP48MMPefTRR7n33nspLS3d\nYib81KlTeeGFF7jhhhu44YYbGD16NE8//XT16yUlJUyZMmWL/W6+7uabb2bs2LHssssuLdMpSVJe\nGeqtVMeOHSkrK2P27NmMGzeOoqIievXqtUWb0aNHM3r06Fr3UdOM9s3XjR8/vrnKlSS1AoZ6K9ap\nU6fqGfCSJNXHc+qSJGWEoS5JUkY4/N7GFF/xcJP38c6kIc1QiSSptfFIXZKkjDDUJUnKCENdkqSM\nMNQlScoIQ12SpIww1CVJyghDXZKkjDDUJUnKCENdkqSMMNQlScoIQ12SpIww1CVJyghDXZKkjDDU\nJUnKCENdkqSMyHuoR8TUiJgfEaW1vL5rRDwaEY9HxO8iYsd81yRJUhblNdQj4jtAu5RSf2DfiNi/\nhmbnAT9PKQ0CPgZOymdNkiRl1ZfzvP8S4L6q5ceBY4DFmzdIKd222dMuwB+33klEjAJGAeyzzz75\nqFOSpDYv38Pv7YEPqpZXAHvU1jAi+gOdUkoLtn4tpXRHSqlvSqlvly5d8lOpJEltXL6P1FcDO1ct\nd6CWPyIiojMwGTgzz/VIkpRZ+T5Sf57KIXeAXsA7Wzeomhj3G+AnKaWlea5HkqTMyneoPwCcHxE/\nB4YBr0XEhK3ajAQOB66KiLKI+Ks81yRJUibldfg9pfRZRJQAJwA3pZQ+BhZt1eYXwC/yWYckSduD\nfJ9TJ6VUwRcz4CVJUp54RTlJkjLCUJckKSMMdUmSMsJQlyQpIwx1SZIywlCXJCkjDHVJkjLCUJck\nKSMMdUmSMsJQlyQpIwx1SZIywlCXJCkjDHVJkjLCUJckKSMMdUmSMsJQlyQpIwx1SZIywlCXJCkj\nDHVJkjLCUJckKSMMdUmSMsJQlyQpIwx1SW3GkiVLmDNnDn/5y18KXYrUKhnqkprNyJEj6d+/PxMm\nTKi1zbJlyxgwYED18/Xr13PqqafyzW9+k2nTplWvf+ONNzj99NOrnz/11FOMHj2axx9/nJNPPjk/\nHZDaOENdUrOYMWMGGzZsYP78+SxZsoTFixdv06aiooLhw4ezZs2a6nWTJ0+mT58+PPPMM9x///2s\nWrWKt956ix//+MesXLmyut0bb7zBfffdx8SJE1m5cqVH61INDHVJzaKsrIxhw4YBMGjQIObNm7dN\nm3bt2jF9+nQ6duxY43YDBw6kvLycr3zlK/z2t7/dYtuLL76YHXfckX//93/n61//OjvttFMeeyO1\nTYa6pGaxZs0aunXrBkDnzp1ZtmzZNm06duzIrrvuWu92Xbt2rTG0ly1bxv3338+BBx6Yhx5IbZ+h\nLqlZdOjQgbVr1wKwevVqNm7BwA7bAAAPaElEQVTc2OzbFRcXM2PGDMrKyvj444+bXrSUMYa6pGbR\np0+f6iH3RYsWUVxc3KzbnX766fzP//wPGzdu5H//939p3759c5QtZcqXC12ApGwYOnQoAwYM4MMP\nP+TRRx/l3nvvpbS0tM6Z8ADDhw/n5JNPZu7cubz++uscddRRNbYrLS3l3HPPZYcdduDiiy/mK1/5\nSj66IbVphrqkZtGxY0fKysqYPXs248aNo6ioiF69etXYtqysrHq5e/fuzJ49m3nz5nHdddfRrl27\nGtsdccQRLFy4MF/lS5lgqEtqNp06daqeyd4Qe+65Z6O2k7Qlz6lLkpQRhrokSRnh8LukvCm+4uFm\n2c87k4Y0y36krPNIXZKkjDDUJamVy+VGObW1GTNmDDNnzgTg7bffZsiQIQwYMIBLL70UqLwe/8kn\nn0zfvn256KKL8tcJtQhDXZJasVxulFNbm7lz5/Lxxx9z6qmnAnD55Zdz9dVXM3fuXN5//33Kysq4\n++67Oe+88ygvL2fVqlWUl5e3aP/UvAx1SWrFcrlRTk1t1q9fzw9/+EOKi4t58MEHAXjzzTc5/PDD\nAejatSsrV67kq1/9Kq+++iqffvop7733HnvvvXcL9Uz5YKhLUiuWy41yampz1113cfDBBzNu3DgW\nLlzI5MmTOeussxg/fjwzZ85k1qxZHHfccRxzzDEsXbqUW265hYMOOojOnTu3aP/UvAx1SWrFcrnh\nTU1tXnzxRUaNGkVRURHf+973mDNnDqWlpQwePJgpU6YwfPhwOnTowPjx47n99tu55pprOPDAA7nz\nzjtbtH9qXoa6JLViudzwpqY2++23H0uWLAGgvLyc7t27A9C7d2/effddxo4dC1ROlHvllVfYsGED\nzz77LBHRAr1Svvg9dakVGzlyJK+//jpDhgyhtLQ0pzaff/45++67L/vuuy8AkydP5tBDDwVg7dq1\n9OjRgyVLlvDJJ59w8cUXs3z5cvbZZx+mTp3KDjvs0GJ9U25yuVHO1m0WLFjAl770JS644ALuvfde\n1q9fz/333w/AzTffzNixY9lll10A+MlPfsKIESNYunQp/fv355xzzilIP9U8DHWpldp8RvMFF1zA\n4sWL2X///etts2rVKs455xxuvPHGbfY5YcIEPvroIwBuvPFGzjzzTL773e9y+eWXc8899/CDH/yg\nJbqmBsjlRjlbt9l1110B+M1vfrPN/saPH7/F8yOPPJLXXnstfx1Qi3L4XWqlGjvrecGCBTz00EMc\neeSRjBw5ks8//xyAP/zhD7z88svVtzataSa0WqdNN8opKipqUhtln6EutVKNnfV8xBFH8MQTT7Bw\n4ULWr1/PI488AsBll13GLbfcUr3tmWeeyaRJk5g9ezZTp07l9NNPb4FeSconh9+lVqqxs5579uzJ\nTjvtBEDfvn1ZvHgxd911F9/61rf4+te/Xr3t+eefT48ePbjttts4+uija5yAJaltMdSlVmrTjOZ+\n/fqxaNEiDjjggJzanH/++Vx11VUccsghPPDAA1x55ZVMmzaN9957j4cffpiXXnqJU045hYceeoje\nvXvz6quvMmPGjAL0UE3RHDfL8UY52WOoS61UY2c99+zZk3PPPZeUEqeddhrHH388xx9/fPU2JSUl\nPPTQQwD8+te/ZsiQIey5554t3j9Jzc9Ql1qpxs563nXXXXn55Zdr3W9ZWVn18ogRI/JVvqQCMNSl\nVmzTjOamtpG0fXD2uyRJDbBixQpmz57Nn/70p0KXsg1DXZK0XWrMfeorKio45ZRTWLhwIcceeyzL\nly9n5cqVDB48mEGDBnHGGWewbt266u2XLVvGYYcdlve+bOLwu9TGOOtZarrGXrHxww8/5Oc//zn9\n+vWjoqKCF154gbfeeouxY8dywgknMHr0aGbNmsVpp50GVF4fYtPXTluCoS5J2u7UdDXGrUO9pjab\nJpc+/fTTLFy4kGuuuYYTTzyxepvly5fTtWtXAJ588knat2/folf5c/hdkrTdaewVGwFSSkyfPp1O\nnTptcROk+fPnU1FRQb9+/Vi3bh3XX389kyZNaoHefMFQlyRtdxp7xUaAiODWW2+lZ8+e/P73vwcq\nJ89dcsklTJs2DYBJkyYxZswYdtttt5boTjVDXZK03WnsfepvvPFG7rrrLgA+/fRTdtttN9atW8fZ\nZ5/NxIkTq+9b/8QTT3DrrbdSUlLCSy+9xIUXXtgi/TLUtd1pzIxXqJzFOmDAgG3annrqqbz00ksA\nfPDBB+y1116UlJRQUlLC8uXLm78Dkpps6NCh3H333YwdO5b77ruPHj16UFpaWmebIUOGMGrUKO6+\n+24GDhzIhg0bGDRoEFOnTuWFF17ghhtuoKSkhOnTp/P0009TVlZGWVkZvXv3ZsqUKS3SLyfKabvS\n2Bmvu+++O8OHD2fNmjVbtL3nnnv4xje+Qe/evQF49tlnueqqqxg9enSL9UlSwzXlPvWzZ8/eot3o\n0aPr/D+/+VUc880jdW1XGnuP8nbt2jF9+nQ6duxY3W7FihVceumldOrUiTlz5gCwYMECpkyZwuGH\nH86VV17ZAj2S1FhZvE+9oa7tSmNnvHbs2LH6r/RN/vEf/5Gzzz6biy66iLvuuovf//73DB48mLKy\nMp577jnmz59f5zXYJam5Ofyu7UpTZrxu7cUXX+SnP/0pRUVFDBs2jNmzZzNx4sTqe5kfdthhLF68\nmJ49e+apN5KaW1u/uJNH6tquNHbGa032228/lixZAkB5eTndu3fnxBNP5KOPPuLPf/4zjz/+OIcc\nckhe+iFJNfFIXduVxt6jvCbjxo3jwgsv5IYbbmCXXXZhxowZ9OzZk2OPPZYdd9yRiy++mAMOOKCl\nuiZJhrq2L02Z8QpbzmLdc889eeSRR7bY9thjj+UPf/hDXvsgSbUx1LXd8R7lkrLKc+qSJGWEoS5J\nUkY4/C7R9r/GIkngkbokSZlhqEuSlBGGuiRJGWGoS5KUEXkP9YiYGhHzI6K0KW0kSVLd8hrqEfEd\noF1KqT+wb0Ts35g2kiSpfpFSyt/OI24BZqWUHomI7wI7p5TubESbUcCoqqcHAP+dt6JrtjvwpxZ+\nz8ZqS7VC26rXWvPDWvOjLdUKbaveQtTaPaXUpb5G+f6eenvgg6rlFcDhjWmTUroDuCMfBeYiIspT\nSn0L9f4N0ZZqhbZVr7Xmh7XmR1uqFdpWva251nyfU18N7Fy13KGW98uljSRJqke+A/R54Jiq5V7A\nO41sI0mS6pHv4fcHgLkRsScwGPhuRExIKZXW0aZfnmtqjIIN/TdCW6oV2la91pof1pofbalWaFv1\nttpa8zpRDiAiOgEnAE+nlD5ubBtJklS3vIe6JElqGU5KkyQpIwz1BoiI8yLizvpbFlZEXBERKyPi\n04i4tND11CUijoiIByPisYgYXOh6chERIyLiV4Wuoy4R8UZEfFz1eL/Q9dQnInaLiHcjYt9C11KX\niLh8s8/144hYHRHXFLqu2kTENRHxftVjRKHrqU1E7BgRv6yq85WIGFDommpSUwZExNCqf7tvRERN\nX9tuUYZ6jiLiFOBWIApdS10ioj9wDnAYcDRwbUTsXdiqahYRuwL3AP8ETAR+XTW/otWKiC7AzYWu\noy4RsQuQUkpFVY+9Cl1TDiYA/5FSWlLoQuqSUrpxs8+1CHgJeLDQddUkIg4BvgV0B/oDN0bEHoWt\nqlajqLxmSTEwArgvIr5S0Iq2UlMGRERXYAqVk7xPoRVMoDPUczcCuL7QReTgz8C5KaUlKaXXqfyK\n4J6FLalWXwNKU0pzUkplwHtAaw+gfwRmF7qIevQCXil0EbmKiMOA7wA3FLqWhoiIbwPLU0qLCl1L\nLQ4CylNKG1JK7wFLgaIC11SbvwJ+kVL6PKVUTuXV2g4ocE1bqykDTgRmp5ReSym9BbwbEf+35Uv7\ngqG+lYg4qGoopUNEHBgRS6v+YjwL+KTQ9W2uplqBJSml16pe3xPYG3i1oIVSa60fpJTui4h2EXEm\nsAPweoFLrfXfQEScQOUfHQX/a3yTWj7XI4FjIuLDiHgrIk4rdJ1Q5/+tfwY+A/4tIkZHRKsYDauj\n3k3+jspRpoKr5d/Bm8BpEfG1quHs3YE3CltprbV2oLI+qj7j7hTokrENzIC9gJc3e74U2K+laq1J\nvr+n3uaklN6IiP8ALqVyCPvylNIqgFbyu6ZaXbVWuQH4ZUppTUEK3Ew9tf4tlbVellLaUKgaN6mp\nVuBz4OdUHlF2K2B5W6il1h2p/Hz/LSL6AQ9GxGMppb+0wloPpfI00Y+pHKmZBCTg9kLVuUk9vwv2\nAvZNKT1VyBo3qeWzfZnKz/Rh4CvAT1NK6wpXZaVaat0N+JeIOBA4A/hDSumd1lJfHRnQDqjY7Pka\nKvtSOCklH1s9qDy38ybwn1ut/wHwq0LXl2Otg4HFQPtC11hfrVWv7UXlPQD2K3SdNdVKZdiMq1ou\naU3/Dur6XKteXwgcWeg6a/lc/x743WavDwceLnSd9X22wDjgmkLXV89nO4LKP+qhclR2NnBEoeus\n7XOlMsx/CqwDzmxt9VWt3yIDgL8Grtrs+c+A7xaydoffa7YzlUc7HSOiXaGLqcc2tVZNjJsCnJda\nwVH6ZraoNSL2i4ieACml94HnaD3n0bb+XE8FLo2Ij4EZwF9FxIxCFriZrT/X70fEjpu93g3YWJjS\ntrH15/oZ8O5mr/8v8GkhCqtFbb8LzqTyapityda19qPqdFZKaSOVk/pay01ItvlcU0q/A54CXqPy\n/1gh5ZoB5VROQtykL1DQb5sY6jX7OZUTol4D/qbAtdRni1ojYifg98DNKaWFBa1sW1t/rnsC06Py\n60xfA44AXixgfZvbotaUUo+U0h6pcsbzd4DpKaXvFLTCL2z9uQ6k8g+QXSLir6k88mktE+e2rnUe\ncFJEdImInYELqfzF3lps87sgIr4KfD2l9HJdGxbA1rW+TeUfn70iooTKb8UUfH5Nldp+x/4EuDJV\nHfYWUK4ZsBDYPyIujMpbhO9Xta5wCj0M09oewHFU/gL8MrAHlX917VPT0EuhH7XUOpzKc5Ifb/Yo\n6FBWXZ8rlf+JPwL+Bzi70HXW92+g6vWS1vLvoI7P9SlgVdXPQwtdZz21fp/Koc5PgF8B7Qpdaz31\nngE8VOj6cqi1O/AbYCWVk86uL3Sd9XyuJwNlrbW+qte2yQDgQOBxYAHQr9D1e5lYSZIywuF3SZIy\nwlCXJCkjDHVJkjLCUJckKSMMdUmSMsJQlyQpIwx1SZIy4v8Dv1F358Orw7QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14ab1b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ivlist = [ivx1, ivx2, ivx3, ivx4, ivx5, ivx6, ivx7, ivx8, ivx9, ivx10] # 各变量IV\n",
    "index = ['x1','x2','x3','x4','x5','x6','x7','x8','x9','x10'] # x轴的标签\n",
    "fig1 = plt.figure(figsize=(8, 8))\n",
    "ax1 = fig1.add_subplot(1, 1, 1)\n",
    "x = np.arange(len(index)) + 1\n",
    "ax1.bar(x, ivlist, width=0.4) # 生成柱状图\n",
    "ax1.set_xticks(x)\n",
    "ax1.set_xticklabels(index, rotation=0, fontsize=12)\n",
    "ax1.set_ylabel('IV(Information Value)', fontsize=12)\n",
    "# 在柱状图上添加数字标签\n",
    "for a, b in zip(x, ivlist):\n",
    "    plt.text(a, b + 0.01, '%.4f' % b, ha='center', va='bottom', fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "通过IV值判断变量预测能力的标准是：\n",
    "\n",
    "**< 0.02: unpredictive**\n",
    "\n",
    "**0.02 to 0.1: weak**\n",
    "\n",
    "**0.1 to 0.3: medium**\n",
    "\n",
    "**0.3 to 0.5: strong**\n",
    "\n",
    "**> 0.5: suspicious**\n",
    "\n",
    "DebtRatio、MonthlyIncome、NumberRealEstateLoansOrLines 和 NumberOfDependents 变量的IV值明显较低"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型分析\n",
    "    WOE转换:\n",
    "        证据权重（Weight of Evidence,WOE）转换可以将Logistic回归模型转变为标准评分卡格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 替换成woe函数\n",
    "def replace_woe(series, cut, woe):\n",
    "    list = []\n",
    "    i = 0\n",
    "    while i < len(series):\n",
    "        value = series[i]\n",
    "        j = len(cut)-2\n",
    "        m = len(cut)-2\n",
    "        while j >= 0:\n",
    "            if value >= cut[j]:\n",
    "                j = -1\n",
    "            else:\n",
    "                j -= 1\n",
    "                m -= 1\n",
    "        list.append(woe[m])\n",
    "        i += 1\n",
    "    return list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas import Series\n",
    "train = pd.read_csv(\"./TrainData.csv\")\n",
    "train['RevolvingUtilizationOfUnsecuredLines'] = Series(replace_woe(train['RevolvingUtilizationOfUnsecuredLines'], cutx1, woex1))\n",
    "train['age'] = Series(replace_woe(train['age'], cutx2, woex2))\n",
    "train['NumberOfTime30-59DaysPastDueNotWorse'] = Series(replace_woe(train['NumberOfTime30-59DaysPastDueNotWorse'], cutx3, woex3))\n",
    "train['DebtRatio'] = Series(replace_woe(train['DebtRatio'], cutx4, woex4))\n",
    "train['MonthlyIncome'] = Series(replace_woe(train['MonthlyIncome'], cutx5, woex5))\n",
    "train['NumberOfOpenCreditLinesAndLoans'] = Series(replace_woe(train['NumberOfOpenCreditLinesAndLoans'], cutx6, woex6))\n",
    "train['NumberOfTimes90DaysLate'] = Series(replace_woe(train['NumberOfTimes90DaysLate'], cutx7, woex7))\n",
    "train['NumberRealEstateLoansOrLines'] = Series(replace_woe(train['NumberRealEstateLoansOrLines'], cutx8, woex8))\n",
    "train['NumberOfTime60-89DaysPastDueNotWorse'] = Series(replace_woe(train['NumberOfTime60-89DaysPastDueNotWorse'], cutx9, woex9))\n",
    "train['NumberOfDependents'] = Series(replace_woe(train['NumberOfDependents'], cutx10, woex10))\n",
    "train.to_csv('Woetrain.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test = pd.read_csv('TestData.csv')\n",
    "# 替换成woe\n",
    "test['RevolvingUtilizationOfUnsecuredLines'] = Series(replace_woe(test['RevolvingUtilizationOfUnsecuredLines'], cutx1, woex1))\n",
    "test['age'] = Series(replace_woe(test['age'], cutx2, woex2))\n",
    "test['NumberOfTime30-59DaysPastDueNotWorse'] = Series(replace_woe(test['NumberOfTime30-59DaysPastDueNotWorse'], cutx3, woex3))\n",
    "test['DebtRatio'] = Series(replace_woe(test['DebtRatio'], cutx4, woex4))\n",
    "test['MonthlyIncome'] = Series(replace_woe(test['MonthlyIncome'], cutx5, woex5))\n",
    "test['NumberOfOpenCreditLinesAndLoans'] = Series(replace_woe(test['NumberOfOpenCreditLinesAndLoans'], cutx6, woex6))\n",
    "test['NumberOfTimes90DaysLate'] = Series(replace_woe(test['NumberOfTimes90DaysLate'], cutx7, woex7))\n",
    "test['NumberRealEstateLoansOrLines'] = Series(replace_woe(test['NumberRealEstateLoansOrLines'], cutx8, woex8))\n",
    "test['NumberOfTime60-89DaysPastDueNotWorse'] = Series(replace_woe(test['NumberOfTime60-89DaysPastDueNotWorse'], cutx9, woex9))\n",
    "test['NumberOfDependents'] = Series(replace_woe(test['NumberOfDependents'], cutx10, woex10))\n",
    "test.to_csv('TestWoeData.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import cross_val_score\n",
    "\n",
    "def cvDictGen(functions, scr, X_train=X_train, Y_train=Y_train, cv=10, verbose=1):\n",
    "    cvDict = {}\n",
    "    for func in functions:\n",
    "        cvScore = cross_val_score(func, X_train, Y_train, cv=cv, verbose=verbose, scoring=scr)\n",
    "        cvDict[str(func).split('(')[0]] = [cvScore.mean(), cvScore.std()]\n",
    "    \n",
    "    return cvDict\n",
    "\n",
    "def cvDictNormalize(cvDict):\n",
    "    cvDictNormalized = {}\n",
    "    for key in cvDict.keys():\n",
    "        for i in cvDict[key]:\n",
    "            cvDictNormalized[key] = ['{:0.2f}'.format((cvDict[key][0]/cvDict[list(cvDict.keys())[0]][0])),\n",
    "                                     '{:0.2f}'.format((cvDict[key][1]/cvDict[list(cvDict.keys())[0]][1]))]\n",
    "    return cvDictNormalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "knMod = KNeighborsClassifier(n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2,\n",
    "                             metric='minkowski', metric_params=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lrMod = LogisticRegression(penalty='l1', dual=False, tol=0.0001, C=1.0, fit_intercept=True,\n",
    "                            intercept_scaling=1, class_weight=None, random_state=None, solver='liblinear', max_iter=100,\n",
    "                            multi_class='ovr', verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adaMod = AdaBoostClassifier(base_estimator=None, n_estimators=200, learning_rate=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gbMod = GradientBoostingClassifier(loss='deviance', learning_rate=0.1, n_estimators=200, subsample=1.0,\n",
    "                                   min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3,\n",
    "                                   init=None, random_state=None, max_features=None, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rfMod = RandomForestClassifier(n_estimators=10, criterion='gini', max_depth=None, min_samples_split=2,\n",
    "                               min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto',\n",
    "                               max_leaf_nodes=None, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:   14.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibLinear][LibLinear][LibLinear][LibLinear][LibLinear][LibLinear][LibLinear][LibLinear][LibLinear][LibLinear]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    7.7s finished\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:  4.4min finished\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:  5.0min finished\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:   32.7s finished\n"
     ]
    }
   ],
   "source": [
    "cvD = cvDictGen(functions=[knMod, lrMod, adaMod, gbMod, rfMod], scr='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AdaBoostClassifier': [0.85833526931636717, 0.0037927708585720095],\n",
       " 'GradientBoostingClassifier': [0.86390791252722998, 0.0033998590788060267],\n",
       " 'KNeighborsClassifier': [0.58889869147901397, 0.011290276615075525],\n",
       " 'LogisticRegression': [0.85008299151287425, 0.0038035141532396718],\n",
       " 'RandomForestClassifier': [0.78156197848479758, 0.011179708285051989]}"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AdaBoostClassifier': ['1.46', '0.34'],\n",
       " 'GradientBoostingClassifier': ['1.47', '0.30'],\n",
       " 'KNeighborsClassifier': ['1.00', '1.00'],\n",
       " 'LogisticRegression': ['1.44', '0.34'],\n",
       " 'RandomForestClassifier': ['1.33', '0.99']}"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvDictNormalize(cvD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AdaBoost 、 GB 、 LR的out-of-the-bag较好"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 最优化超参数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AdaBoost模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.grid_search import RandomizedSearchCV\n",
    "from scipy.stats import randintt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ada_param = {'n_estimators': [10,50,100,200,400],\n",
    "                 'learning_rate': [0.1, 0.05]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 5 candidates, totalling 15 fits\n",
      "[CV] n_estimators=10, learning_rate=0.1 ..............................\n",
      "[CV] ..................... n_estimators=10, learning_rate=0.1 -   0.9s\n",
      "[CV] n_estimators=10, learning_rate=0.1 ..............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ..................... n_estimators=10, learning_rate=0.1 -   0.9s\n",
      "[CV] n_estimators=10, learning_rate=0.1 ..............................\n",
      "[CV] ..................... n_estimators=10, learning_rate=0.1 -   0.9s\n",
      "[CV] n_estimators=400, learning_rate=0.05 ............................\n",
      "[CV] ................... n_estimators=400, learning_rate=0.05 -  39.1s\n",
      "[CV] n_estimators=400, learning_rate=0.05 ............................\n",
      "[CV] ................... n_estimators=400, learning_rate=0.05 -  39.4s\n",
      "[CV] n_estimators=400, learning_rate=0.05 ............................\n",
      "[CV] ................... n_estimators=400, learning_rate=0.05 -  39.4s\n",
      "[CV] n_estimators=50, learning_rate=0.05 .............................\n",
      "[CV] .................... n_estimators=50, learning_rate=0.05 -   4.9s\n",
      "[CV] n_estimators=50, learning_rate=0.05 .............................\n",
      "[CV] .................... n_estimators=50, learning_rate=0.05 -   4.9s\n",
      "[CV] n_estimators=50, learning_rate=0.05 .............................\n",
      "[CV] .................... n_estimators=50, learning_rate=0.05 -   5.0s\n",
      "[CV] n_estimators=100, learning_rate=0.1 .............................\n",
      "[CV] .................... n_estimators=100, learning_rate=0.1 -  10.0s\n",
      "[CV] n_estimators=100, learning_rate=0.1 .............................\n",
      "[CV] .................... n_estimators=100, learning_rate=0.1 -   9.8s\n",
      "[CV] n_estimators=100, learning_rate=0.1 .............................\n",
      "[CV] .................... n_estimators=100, learning_rate=0.1 -  10.4s\n",
      "[CV] n_estimators=400, learning_rate=0.1 .............................\n",
      "[CV] .................... n_estimators=400, learning_rate=0.1 -  39.5s\n",
      "[CV] n_estimators=400, learning_rate=0.1 .............................\n",
      "[CV] .................... n_estimators=400, learning_rate=0.1 -  39.7s\n",
      "[CV] n_estimators=400, learning_rate=0.1 .............................\n",
      "[CV] .................... n_estimators=400, learning_rate=0.1 -  37.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:  4.7min finished\n"
     ]
    }
   ],
   "source": [
    "randomizedSearchAda = RandomizedSearchCV(estimator=adaMod, param_distributions=ada_param, n_iter=5,\n",
    "                                   scoring='roc_auc', fit_params=None, cv=None, verbose=2).fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'learning_rate': 0.1, 'n_estimators': 400}, 0.8594144267242873)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randomizedSearchAda.best_params_, randomizedSearchAda.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GB模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbParams = {'loss' : ['deviance', 'exponential'],\n",
    "            'n_estimators': [10,50,100,200,400],\n",
    "            'max_depth': randint(1,5),\n",
    "            'learning_rate':[0.1, 0.05]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] learning_rate=0.05, loss=exponential, max_depth=4, n_estimators=10 \n",
      "[CV]  learning_rate=0.05, loss=exponential, max_depth=4, n_estimators=10 -   1.6s\n",
      "[CV] learning_rate=0.05, loss=exponential, max_depth=4, n_estimators=10 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.6s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.05, loss=exponential, max_depth=4, n_estimators=10 -   1.5s\n",
      "[CV] learning_rate=0.05, loss=exponential, max_depth=4, n_estimators=10 \n",
      "[CV]  learning_rate=0.05, loss=exponential, max_depth=4, n_estimators=10 -   1.5s\n",
      "[CV] learning_rate=0.1, loss=deviance, max_depth=3, n_estimators=50 ..\n",
      "[CV]  learning_rate=0.1, loss=deviance, max_depth=3, n_estimators=50 -   5.5s\n",
      "[CV] learning_rate=0.1, loss=deviance, max_depth=3, n_estimators=50 ..\n",
      "[CV]  learning_rate=0.1, loss=deviance, max_depth=3, n_estimators=50 -   5.4s\n",
      "[CV] learning_rate=0.1, loss=deviance, max_depth=3, n_estimators=50 ..\n",
      "[CV]  learning_rate=0.1, loss=deviance, max_depth=3, n_estimators=50 -   5.3s\n",
      "[CV] learning_rate=0.1, loss=exponential, max_depth=1, n_estimators=100 \n",
      "[CV]  learning_rate=0.1, loss=exponential, max_depth=1, n_estimators=100 -   4.1s\n",
      "[CV] learning_rate=0.1, loss=exponential, max_depth=1, n_estimators=100 \n",
      "[CV]  learning_rate=0.1, loss=exponential, max_depth=1, n_estimators=100 -   4.1s\n",
      "[CV] learning_rate=0.1, loss=exponential, max_depth=1, n_estimators=100 \n",
      "[CV]  learning_rate=0.1, loss=exponential, max_depth=1, n_estimators=100 -   4.1s\n",
      "[CV] learning_rate=0.1, loss=exponential, max_depth=2, n_estimators=200 \n",
      "[CV]  learning_rate=0.1, loss=exponential, max_depth=2, n_estimators=200 -  13.8s\n",
      "[CV] learning_rate=0.1, loss=exponential, max_depth=2, n_estimators=200 \n",
      "[CV]  learning_rate=0.1, loss=exponential, max_depth=2, n_estimators=200 -  13.8s\n",
      "[CV] learning_rate=0.1, loss=exponential, max_depth=2, n_estimators=200 \n",
      "[CV]  learning_rate=0.1, loss=exponential, max_depth=2, n_estimators=200 -  13.9s\n",
      "[CV] learning_rate=0.05, loss=deviance, max_depth=1, n_estimators=200 \n",
      "[CV]  learning_rate=0.05, loss=deviance, max_depth=1, n_estimators=200 -   8.0s\n",
      "[CV] learning_rate=0.05, loss=deviance, max_depth=1, n_estimators=200 \n",
      "[CV]  learning_rate=0.05, loss=deviance, max_depth=1, n_estimators=200 -   7.7s\n",
      "[CV] learning_rate=0.05, loss=deviance, max_depth=1, n_estimators=200 \n",
      "[CV]  learning_rate=0.05, loss=deviance, max_depth=1, n_estimators=200 -   7.7s\n",
      "[CV] learning_rate=0.1, loss=deviance, max_depth=4, n_estimators=200 .\n",
      "[CV]  learning_rate=0.1, loss=deviance, max_depth=4, n_estimators=200 -  29.7s\n",
      "[CV] learning_rate=0.1, loss=deviance, max_depth=4, n_estimators=200 .\n",
      "[CV]  learning_rate=0.1, loss=deviance, max_depth=4, n_estimators=200 -  29.7s\n",
      "[CV] learning_rate=0.1, loss=deviance, max_depth=4, n_estimators=200 .\n",
      "[CV]  learning_rate=0.1, loss=deviance, max_depth=4, n_estimators=200 -  27.6s\n",
      "[CV] learning_rate=0.05, loss=deviance, max_depth=1, n_estimators=50 .\n",
      "[CV]  learning_rate=0.05, loss=deviance, max_depth=1, n_estimators=50 -   2.1s\n",
      "[CV] learning_rate=0.05, loss=deviance, max_depth=1, n_estimators=50 .\n",
      "[CV]  learning_rate=0.05, loss=deviance, max_depth=1, n_estimators=50 -   1.7s\n",
      "[CV] learning_rate=0.05, loss=deviance, max_depth=1, n_estimators=50 .\n",
      "[CV]  learning_rate=0.05, loss=deviance, max_depth=1, n_estimators=50 -   1.7s\n",
      "[CV] learning_rate=0.05, loss=deviance, max_depth=2, n_estimators=400 \n",
      "[CV]  learning_rate=0.05, loss=deviance, max_depth=2, n_estimators=400 -  23.7s\n",
      "[CV] learning_rate=0.05, loss=deviance, max_depth=2, n_estimators=400 \n",
      "[CV]  learning_rate=0.05, loss=deviance, max_depth=2, n_estimators=400 -  23.1s\n",
      "[CV] learning_rate=0.05, loss=deviance, max_depth=2, n_estimators=400 \n",
      "[CV]  learning_rate=0.05, loss=deviance, max_depth=2, n_estimators=400 -  23.1s\n",
      "[CV] learning_rate=0.1, loss=deviance, max_depth=4, n_estimators=200 .\n",
      "[CV]  learning_rate=0.1, loss=deviance, max_depth=4, n_estimators=200 -  25.0s\n",
      "[CV] learning_rate=0.1, loss=deviance, max_depth=4, n_estimators=200 .\n",
      "[CV]  learning_rate=0.1, loss=deviance, max_depth=4, n_estimators=200 -  24.0s\n",
      "[CV] learning_rate=0.1, loss=deviance, max_depth=4, n_estimators=200 .\n",
      "[CV]  learning_rate=0.1, loss=deviance, max_depth=4, n_estimators=200 -  24.7s\n",
      "[CV] learning_rate=0.1, loss=deviance, max_depth=3, n_estimators=10 ..\n",
      "[CV]  learning_rate=0.1, loss=deviance, max_depth=3, n_estimators=10 -   0.8s\n",
      "[CV] learning_rate=0.1, loss=deviance, max_depth=3, n_estimators=10 ..\n",
      "[CV]  learning_rate=0.1, loss=deviance, max_depth=3, n_estimators=10 -   0.8s\n",
      "[CV] learning_rate=0.1, loss=deviance, max_depth=3, n_estimators=10 ..\n",
      "[CV]  learning_rate=0.1, loss=deviance, max_depth=3, n_estimators=10 -   0.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:  5.7min finished\n"
     ]
    }
   ],
   "source": [
    "randomizedSearchGB = RandomizedSearchCV(estimator=gbMod, param_distributions=gbParams, n_iter=10,\n",
    "                                   scoring='roc_auc', fit_params=None, cv=None, verbose=2).fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'learning_rate': 0.1,\n",
       "  'loss': 'exponential',\n",
       "  'max_depth': 2,\n",
       "  'n_estimators': 200},\n",
       " 0.8631270429865617)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randomizedSearchGB.best_params_, randomizedSearchGB.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bestGbModFitted = randomizedSearchGB.best_estimator_.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bestAdaModFitted = randomizedSearchAda.best_estimator_.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:  2.6min finished\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:  6.9min finished\n"
     ]
    }
   ],
   "source": [
    "cvDictHPO = cvDictGen(functions=[bestGbModFitted, bestAdaModFitted], scr='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AdaBoostClassifier': ['1.00', '1.12'],\n",
       " 'GradientBoostingClassifier': ['1.00', '1.00']}"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvDictNormalize(cvDictHPO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotCvRocCurve(X, y, classifier, nfolds=5):\n",
    "    \n",
    "    from sklearn.metrics import roc_curve, auc\n",
    "    from sklearn.cross_validation import StratifiedKFold\n",
    "    import matplotlib.pyplot as plt\n",
    "    from scipy import interp\n",
    "\n",
    "    cv = StratifiedKFold(y, n_folds=nfolds)\n",
    "\n",
    "    mean_tpr = 0.0\n",
    "    mean_fpr = np.linspace(0, 1, 100)\n",
    "    all_tpr = []\n",
    "\n",
    "    for i, (train, test) in enumerate(cv):\n",
    "        probas_ = classifier.fit(X.iloc[train], y.iloc[train]).predict_proba(X.iloc[test])\n",
    "\n",
    "        fpr, tpr, thresholds = roc_curve(y.iloc[test], probas_[:, 1])\n",
    "        mean_tpr += interp(mean_fpr, fpr, tpr)\n",
    "        mean_tpr[0] = 0.0\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "        plt.plot(fpr, tpr, lw=1, label='ROC fold %d (area = %0.2f)' % (i, roc_auc))\n",
    "\n",
    "    plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Luck')\n",
    "\n",
    "    mean_tpr /= len(cv)\n",
    "    mean_tpr[-1] = 1.0\n",
    "    mean_auc = auc(mean_fpr, mean_tpr)\n",
    "    plt.plot(mean_fpr, mean_tpr, 'k--',\n",
    "             label='Mean ROC (area = %0.2f)' % mean_auc, lw=2)\n",
    "\n",
    "    plt.xlim([-0.05, 1.05])\n",
    "    plt.ylim([-0.05, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('CV ROC curve')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    fig = plt.gcf()\n",
    "    fig.set_size_inches(15,5)\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "def rocZeroOne(y_true, y_predicted_porba):\n",
    "    \n",
    "    from sklearn.metrics import roc_curve\n",
    "    from scipy.spatial.distance import euclidean\n",
    "    \n",
    "    fpr, tpr, thresholds = roc_curve(y_true, y_predicted_porba[:, 1])\n",
    "    \n",
    "    best = [0, 1]\n",
    "    dist = []\n",
    "    for (x, y) in zip(fpr, tpr):\n",
    "        dist.append([euclidean([x,y], best)])\n",
    "\n",
    "    bestPoint = [fpr[dist.index(min(dist))], tpr[dist.index(min(dist))]]\n",
    "    \n",
    "    bestCutOff1 = thresholds[list(fpr).index(bestPoint[0])]\n",
    "    bestCutOff2 = thresholds[list(tpr).index(bestPoint[1])]\n",
    "    \n",
    "    print('\\n' + 'ROC曲线最佳点位置: TPR = {:0.3f}%, FPR = {:0.3f}%'.format(bestPoint[1]*100, bestPoint[0]*100))\n",
    "    print('\\n' + '最佳截止点: {:0.4f}'.format(bestCutOff1))\n",
    "\n",
    "    plt.plot(dist)\n",
    "    plt.xlabel('Index')\n",
    "    plt.ylabel('Euclidean Distance to the perfect [0,1]')\n",
    "    fig = plt.gcf()\n",
    "    fig.set_size_inches(15,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3gAAAFICAYAAAACi1zKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x176bc320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotCvRocCurve(X, Y, randomizedSearchGB.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best point on the ROC: TPR = 76.615%, FPR = 21.597%\n",
      "\n",
      "Best Cut-Off point: 0.0722\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3sAAAE8CAYAAAB5D6geAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUrEnIiIiIiLSB/03evE2F1vn/HMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14703550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rocZeroOne(Y_test, randomizedSearchGB.predict_proba(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3gAAAFICAYAAAACi1zKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17692e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotCvRocCurve(X, Y, gridSearchAda.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best point on the ROC: TPR = 75.889%, FPR = 21.769%\n",
      "\n",
      "Best Cut-Off point: 0.4834\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3sAAAE8CAYAAAB5D6geAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIiI5KD/D9FVlBkdE+ZsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18e33a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rocZeroOne(Y_test, gridSearchAda.predict_proba(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LR模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lrMod.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda\\anaconda\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.188722\n",
      "         Iterations 8\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:       SeriousDlqin2yrs   No. Observations:               101893\n",
      "Model:                          Logit   Df Residuals:                   101885\n",
      "Method:                           MLE   Df Model:                            7\n",
      "Date:                Thu, 25 Oct 2018   Pseudo R-squ.:                  0.2409\n",
      "Time:                        10:57:38   Log-Likelihood:                -19229.\n",
      "converged:                       True   LL-Null:                       -25333.\n",
      "                                        LLR p-value:                     0.000\n",
      "========================================================================================================\n",
      "                                           coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------------------------\n",
      "const                                   -9.2342      0.109    -84.953      0.000      -9.447      -9.021\n",
      "RevolvingUtilizationOfUnsecuredLines     0.6295      0.016     40.463      0.000       0.599       0.660\n",
      "age                                      0.4615      0.031     14.856      0.000       0.401       0.522\n",
      "NumberOfTime30-59DaysPastDueNotWorse     1.1004      0.031     35.974      0.000       1.040       1.160\n",
      "MonthlyIncome                            0.3725      0.063      5.895      0.000       0.249       0.496\n",
      "NumberOfOpenCreditLinesAndLoans          0.5254      0.058      9.084      0.000       0.412       0.639\n",
      "NumberOfTimes90DaysLate                  1.5906      0.043     37.308      0.000       1.507       1.674\n",
      "NumberOfTime60-89DaysPastDueNotWorse     1.1133      0.044     25.390      0.000       1.027       1.199\n",
      "========================================================================================================\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "# 导入数据\n",
    "data = pd.read_csv('./Woetrain.csv')\n",
    "#应变量\n",
    "Y = data['SeriousDlqin2yrs']\n",
    "#自变量，剔除对因变量影响不明显的变量\n",
    "X = data.drop(['SeriousDlqin2yrs','DebtRatio','NumberRealEstateLoansOrLines','NumberOfDependents'],axis=1)\n",
    "# X = data.drop(['SeriousDlqin2yrs','DebtRatio','MonthlyIncome', 'NumberOfOpenCreditLinesAndLoans','NumberRealEstateLoansOrLines','NumberOfDependents'],axis=1)\n",
    "X1 = sm.add_constant(X)\n",
    "logit = sm.Logit(Y, X1)\n",
    "result = logit.fit()\n",
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEHCAYAAABFroqmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH0oOIiKyDyUHERHZx/8DJXrNfseikOwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119cfb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test = pd.read_csv('./TestWoeData.csv')\n",
    "Y_test = test['SeriousDlqin2yrs']\n",
    "# X_test = test.drop(['SeriousDlqin2yrs', 'DebtRatio', 'MonthlyIncome', 'NumberOfOpenCreditLinesAndLoans','NumberRealEstateLoansOrLines', 'NumberOfDependents'], axis=1)\n",
    "X_test = test.drop(['SeriousDlqin2yrs', 'DebtRatio', 'NumberRealEstateLoansOrLines', 'NumberOfDependents'], axis=1)\n",
    "X3 = sm.add_constant(X_test)\n",
    "resu = result.predict(X3)\n",
    "fpr, tpr, threshold = roc_curve(Y_test, resu)\n",
    "rocauc = auc(fpr, tpr)\n",
    "plt.plot(fpr, tpr, 'b', label='AUC = %0.2f' % rocauc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1], 'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('真正率')\n",
    "plt.xlabel('假正率')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math\n",
    "# coe为逻辑回归模型的系数\n",
    "coe = [9.2342, 0.6295, 0.4615, 1.1004, 0.3725, 0.5254, 1.5906, 1.1133]\n",
    "\n",
    "p = 20 / math.log(2)\n",
    "q = 600 - 20 * math.log(20) / math.log(2)\n",
    "baseScore = round(q + p * coe[0], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "780.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baseScore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_score(coe, woe, factor):\n",
    "    scores = []\n",
    "    for w in woe:\n",
    "        score = round(coe * w * factor, 0)\n",
    "        scores.append(score)\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x1 = get_score(coe[1], woex1, p)\n",
    "x2 = get_score(coe[2], woex2, p)\n",
    "x3 = get_score(coe[3], woex3, p)\n",
    "x5 = get_score(coe[4], woex5, p)\n",
    "x6 = get_score(coe[5], woex6, p)\n",
    "x7 = get_score(coe[6], woex7, p)\n",
    "x9 = get_score(coe[7], woex9, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-25.0, -22.0, -5.0, 20.0] [8.0, 5.0, 4.0, 3.0, 1.0, -2.0, -6.0, -12.0, -14.0] [-16.0, 29.0, 55.0, 75.0, 83.0] [2.0, 1.0, -4.0] [17.0, 5.0, 2.0, -0.0, -2.0] [-17.0, 88.0, 122.0, 151.0] [-9.0, 58.0, 87.0, 100.0]\n"
     ]
    }
   ],
   "source": [
    "print(x1, x2, x3, x5, x6, x7, x9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def compute_score(series, cut, score):\n",
    "    list = []\n",
    "    i = 0\n",
    "    while i < len(series):\n",
    "        value = series[i]\n",
    "        j = len(cut) - 2\n",
    "        m = len(cut) - 2\n",
    "        while j >= 0:\n",
    "            if value >= cut[j]:\n",
    "                j = -1\n",
    "            else:\n",
    "                j -= 1\n",
    "                m -= 1\n",
    "        list.append(score[m])\n",
    "        i += 1\n",
    "    return list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test1 = pd.read_csv('TestData.csv')\n",
    "test1['BaseScore'] = Series(np.zeros(len(test1))) + baseScore\n",
    "test1['x1'] = Series(compute_score(test1['RevolvingUtilizationOfUnsecuredLines'], cutx1, x1))\n",
    "test1['x2'] = Series(compute_score(test1['age'], cutx2, x2))\n",
    "test1['x3'] = Series(compute_score(test1['NumberOfTime30-59DaysPastDueNotWorse'], cutx3, x3))\n",
    "test1['x5'] = Series(compute_score(test1['MonthlyIncome'], cutx5, x5))\n",
    "test1['x6'] = Series(compute_score(test1['NumberOfOpenCreditLinesAndLoans'], cutx6, x6))\n",
    "test1['x7'] = Series(compute_score(test1['NumberOfTimes90DaysLate'], cutx7, x7))\n",
    "test1['x9'] = Series(compute_score(test1['NumberOfTime60-89DaysPastDueNotWorse'], cutx9, x9))\n",
    "test1['Score'] = test1['x1'] + test1['x2'] + test1['x3'] + test1['x7'] +test1['x9']  + baseScore\n",
    "test1.to_csv('ScoreData.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "ScoreData = pd.read_csv(\"ScoreData.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 43669 entries, 0 to 43668\n",
      "Data columns (total 20 columns):\n",
      "SeriousDlqin2yrs                        43669 non-null int64\n",
      "RevolvingUtilizationOfUnsecuredLines    43669 non-null float64\n",
      "age                                     43669 non-null int64\n",
      "NumberOfTime30-59DaysPastDueNotWorse    43669 non-null float64\n",
      "DebtRatio                               43669 non-null float64\n",
      "MonthlyIncome                           43669 non-null float64\n",
      "NumberOfOpenCreditLinesAndLoans         43669 non-null int64\n",
      "NumberOfTimes90DaysLate                 43669 non-null float64\n",
      "NumberRealEstateLoansOrLines            43669 non-null int64\n",
      "NumberOfTime60-89DaysPastDueNotWorse    43669 non-null float64\n",
      "NumberOfDependents                      43669 non-null float64\n",
      "BaseScore                               43669 non-null float64\n",
      "x1                                      43669 non-null float64\n",
      "x2                                      43669 non-null float64\n",
      "x3                                      43669 non-null float64\n",
      "x5                                      43669 non-null float64\n",
      "x6                                      43669 non-null float64\n",
      "x7                                      43669 non-null float64\n",
      "x9                                      43669 non-null float64\n",
      "Score                                   43669 non-null float64\n",
      "dtypes: float64(16), int64(4)\n",
      "memory usage: 6.7 MB\n"
     ]
    }
   ],
   "source": [
    "ScoreData.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <th>BaseScore</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x5</th>\n",
       "      <th>x6</th>\n",
       "      <th>x7</th>\n",
       "      <th>x9</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0.781021</td>\n",
       "      <td>58</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.445031</td>\n",
       "      <td>1136.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1056.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>0.573681</td>\n",
       "      <td>63</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.432925</td>\n",
       "      <td>4166.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>963.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.357243</td>\n",
       "      <td>3568.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1056.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1</td>\n",
       "      <td>0.950578</td>\n",
       "      <td>38</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.165383</td>\n",
       "      <td>7400.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1006.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1</td>\n",
       "      <td>0.081235</td>\n",
       "      <td>49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.289871</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>934.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "5                  1                              0.781021   58   \n",
       "23                 1                              0.573681   63   \n",
       "78                 1                              1.000000   50   \n",
       "80                 1                              0.950578   38   \n",
       "88                 1                              0.081235   49   \n",
       "\n",
       "    NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "5                                    5.0   1.445031         1136.0   \n",
       "23                                   0.0   0.432925         4166.0   \n",
       "78                                   3.0   0.357243         3568.0   \n",
       "80                                   2.0   0.165383         7400.0   \n",
       "88                                   0.0   0.289871        10000.0   \n",
       "\n",
       "    NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "5                                 5                      0.0   \n",
       "23                                5                      0.0   \n",
       "78                                2                      1.0   \n",
       "80                                4                      0.0   \n",
       "88                                7                      0.0   \n",
       "\n",
       "    NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "5                              1                                   2.0   \n",
       "23                             1                                   0.0   \n",
       "78                             1                                   0.0   \n",
       "80                             1                                   0.0   \n",
       "88                             1                                   0.0   \n",
       "\n",
       "    NumberOfDependents  BaseScore    x1    x2    x3   x5   x6     x7    x9  \\\n",
       "5                  3.0      780.0  20.0  -2.0  83.0  2.0 -2.0   88.0  87.0   \n",
       "23                 1.0      780.0  20.0 -12.0  29.0  1.0 -2.0   88.0  58.0   \n",
       "78                 2.0      780.0  20.0   1.0  75.0  1.0  2.0  122.0  58.0   \n",
       "80                 3.0      780.0  20.0   5.0  55.0 -4.0 -0.0   88.0  58.0   \n",
       "88                 1.0      780.0 -22.0   1.0  29.0 -4.0 -2.0   88.0  58.0   \n",
       "\n",
       "     Score  \n",
       "5   1056.0  \n",
       "23   963.0  \n",
       "78  1056.0  \n",
       "80  1006.0  \n",
       "88   934.0  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ScoreData[ScoreData.SeriousDlqin2yrs == 1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
